{"cells":[{"cell_type":"markdown","metadata":{"id":"orrpsbmYU7pL"},"source":["# Tutorial on `edu-convokit` for the Amber dataset\n","\n","Welcome to the tutorial on `edu-convokit` for the [Amber dataset](https://github.com/laurenceholt/amber/tree/main). This tutorial will walk you through the process of using `edu-convokit` to pre-process, annotate and analyze the Amber dataset.\n","\n","If you are looking for a tutorial on the individual components of `edu-convokit`, please refer to the following tutorials to get started:\n","- [Text Pre-processing Colab](https://colab.research.google.com/drive/1a-EwYwkNYHSNcNThNTXe6DNpsis0bpQK)\n","- [Annotation Colab](https://colab.research.google.com/drive/1rBwEctFtmQowZHxralH2OGT5uV0zRIQw)\n","- [Analysis Colab](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe)\n","\n","This tutorial will use all of the components!"]},{"cell_type":"markdown","metadata":{"id":"XvZ0-3nfU7pM"},"source":["## Installation\n","\n","Let's start by installing `edu-convokit` and importing the necessary modules.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26345,"status":"ok","timestamp":1703933148182,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"nGJhCqQYU7pN","outputId":"69afe5e0-9e09-441e-fe0b-ea39a207042a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting git+https://github.com/rosewang2008/edu-convokit.git\n","  Cloning https://github.com/rosewang2008/edu-convokit.git to /tmp/pip-req-build-repqq1x3\n","  Running command git clone --filter=blob:none --quiet https://github.com/rosewang2008/edu-convokit.git /tmp/pip-req-build-repqq1x3\n","  Resolved https://github.com/rosewang2008/edu-convokit.git to commit 2c36eabaf3d4dff1d8c1e89ae4f175ec80617f7e\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (4.66.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (1.23.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (1.11.4)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (3.8.1)\n","Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (2.1.0+cu121)\n","Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (4.35.2)\n","Collecting clean-text (from edu-convokit==0.0.1)\n","  Downloading clean_text-0.6.0-py3-none-any.whl (11 kB)\n","Requirement already satisfied: openpyxl in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (3.1.2)\n","Requirement already satisfied: spacy in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (3.6.1)\n","Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (4.3.2)\n","Collecting num2words==0.5.10 (from edu-convokit==0.0.1)\n","  Downloading num2words-0.5.10-py3-none-any.whl (101 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.6/101.6 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (1.2.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (3.7.1)\n","Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (0.12.2)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) (1.5.3)\n","Collecting docopt>=0.6.2 (from num2words==0.5.10->edu-convokit==0.0.1)\n","  Downloading docopt-0.6.2.tar.gz (25 kB)\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting emoji<2.0.0,>=1.0.0 (from clean-text->edu-convokit==0.0.1)\n","  Downloading emoji-1.7.0.tar.gz (175 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.4/175.4 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting ftfy<7.0,>=6.0 (from clean-text->edu-convokit==0.0.1)\n","  Downloading ftfy-6.1.3-py3-none-any.whl (53 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.4/53.4 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim->edu-convokit==0.0.1) (6.4.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (1.2.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (4.46.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (23.2)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (3.1.1)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->edu-convokit==0.0.1) (2.8.2)\n","Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->edu-convokit==0.0.1) (8.1.7)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->edu-convokit==0.0.1) (1.3.2)\n","Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->edu-convokit==0.0.1) (2023.6.3)\n","Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl->edu-convokit==0.0.1) (1.1.0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->edu-convokit==0.0.1) (2023.3.post1)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->edu-convokit==0.0.1) (3.2.0)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (3.0.12)\n","Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (1.0.5)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (1.0.10)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (2.0.8)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (3.0.9)\n","Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (8.1.12)\n","Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (1.1.2)\n","Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (2.4.8)\n","Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (2.0.10)\n","Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (0.9.0)\n","Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (0.10.3)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (2.31.0)\n","Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (1.10.13)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (3.1.2)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (67.7.2)\n","Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy->edu-convokit==0.0.1) (3.3.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (3.13.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (4.5.0)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (3.2.1)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (2023.6.0)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->edu-convokit==0.0.1) (2.1.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers->edu-convokit==0.0.1) (0.19.4)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers->edu-convokit==0.0.1) (6.0.1)\n","Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->edu-convokit==0.0.1) (0.15.0)\n","Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers->edu-convokit==0.0.1) (0.4.1)\n","Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy<7.0,>=6.0->clean-text->edu-convokit==0.0.1) (0.2.12)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->edu-convokit==0.0.1) (1.16.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy->edu-convokit==0.0.1) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy->edu-convokit==0.0.1) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy->edu-convokit==0.0.1) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy->edu-convokit==0.0.1) (2023.11.17)\n","Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy->edu-convokit==0.0.1) (0.7.11)\n","Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy->edu-convokit==0.0.1) (0.1.4)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy->edu-convokit==0.0.1) (2.1.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->edu-convokit==0.0.1) (1.3.0)\n","Building wheels for collected packages: edu-convokit, docopt, emoji\n","  Building wheel for edu-convokit (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for edu-convokit: filename=edu_convokit-0.0.1-py3-none-any.whl size=24909 sha256=3e71c896ae285efe3570837daebb1e05f2855b510db11ebff66b503b2e1e7939\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-hve93bfu/wheels/29/43/ec/d2472df0eb2af8f1e7d67d0710a4b3eb93fe983b15f8d7b841\n","  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=cd3e6e02d727798745a61ddf5b510db593fc4f6b164c6f242e607477dfb090ce\n","  Stored in directory: /root/.cache/pip/wheels/fc/ab/d4/5da2067ac95b36618c629a5f93f809425700506f72c9732fac\n","  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for emoji: filename=emoji-1.7.0-py3-none-any.whl size=171033 sha256=a5a47ae4916b773f7e0049304c63c50d066bee6292e0860693efecda135b802f\n","  Stored in directory: /root/.cache/pip/wheels/31/8a/8c/315c9e5d7773f74b33d5ed33f075b49c6eaeb7cedbb86e2cf8\n","Successfully built edu-convokit docopt emoji\n","Installing collected packages: emoji, docopt, num2words, ftfy, clean-text, edu-convokit\n","Successfully installed clean-text-0.6.0 docopt-0.6.2 edu-convokit-0.0.1 emoji-1.7.0 ftfy-6.1.3 num2words-0.5.10\n"]}],"source":["!pip install git+https://github.com/rosewang2008/edu-convokit.git\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25557,"status":"ok","timestamp":1703933173736,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"lMFU9vJgU7pN","outputId":"3fec19d2-1f41-47cb-8f4b-f5ac3fcdc97e"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:root:Since the GPL-licensed package `unidecode` is not installed, using Python's `unicodedata` package which yields worse results.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]}],"source":["from edu_convokit.preprocessors import TextPreprocessor\n","from edu_convokit.annotation import Annotator\n","from edu_convokit.analyzers import (\n","    QualitativeAnalyzer,\n","    QuantitativeAnalyzer,\n","    LexicalAnalyzer,\n","    TemporalAnalyzer\n",")\n","# For helping us load data\n","from edu_convokit import utils\n","\n","import os\n","import tqdm"]},{"cell_type":"markdown","metadata":{"id":"XWo_LjzOU7pO"},"source":["## 📑 Data\n","\n","Let's download the dataset under `raw_data/`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":793,"status":"ok","timestamp":1703933279655,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"ORG_wOuDU7pO","outputId":"a1b8f1ff-e612-4511-817e-cf4a35664676"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-12-30 10:47:59--  https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/amber.zip\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 547335 (535K) [application/zip]\n","Saving to: ‘amber.zip’\n","\n","amber.zip           100%[===================>] 534.51K  --.-KB/s    in 0.04s   \n","\n","2023-12-30 10:47:59 (11.9 MB/s) - ‘amber.zip’ saved [547335/547335]\n","\n"]}],"source":["# We will put the data here:\n","DATA_DIR = \"raw_data\"\n","!mkdir -p $DATA_DIR\n","\n","# We will put the annotated data here:\n","ANNOTATIONS_DIR = \"annotations\"\n","!mkdir -p $ANNOTATIONS_DIR\n","\n","# # Download the data\n","!wget \"https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/amber.zip\"\n","\n","# # Unzip the data\n","!unzip -n -q amber.zip -d $DATA_DIR\n","\n","# Data directory is then raw_data/amber\n","DATA_DIR = \"raw_data/amber\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fouesc5MU7pO"},"outputs":[],"source":["# We'll set the important variables specific to this dataset. If you open one of the files, you'll see that the\n","# speaker and text columns are defined as:\n","TEXT_COLUMN = \"dialogue\"\n","SPEAKER_COLUMN = \"speaker\"\n","\n","# We will also define the annotation columns.\n","# For the purposes of this tutorial, we will only be using talktime, student_reasoning, and uptake.\n","TALK_TIME_COLUMN = \"talktime\"\n","STUDENT_REASONING_COLUMN = \"student_reasoning\"\n","UPTAKE_COLUMN = \"uptake\""]},{"cell_type":"markdown","metadata":{"id":"BQQHbS-9U7pO"},"source":["One thing that will be important is knowing how the teacher/tutor and student are represented in the dataset.\n","Let's load some examples and see how they are represented."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":101,"status":"ok","timestamp":1703933296967,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"c0hX2oMzU7pO","outputId":"87f64d4e-0147-419e-f5d8-b15e3f91a69f"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ef870776-b89a-417a-99c1-38d9bbab4120\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>start</th>\n","      <th>stop</th>\n","      <th>speaker</th>\n","      <th>dialogue</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00:00.00</td>\n","      <td>00:06.00</td>\n","      <td>Tutor</td>\n","      <td>All right. Do you see the tools on the left-ha...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00:06.00</td>\n","      <td>00:07.00</td>\n","      <td>Student</td>\n","      <td>Yes.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00:07.00</td>\n","      <td>00:19.00</td>\n","      <td>Tutor</td>\n","      <td>All right. We're going to take a look at those...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>00:19.00</td>\n","      <td>00:31.00</td>\n","      <td>Tutor</td>\n","      <td>So the third button down is a pencil. If you c...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00:31.00</td>\n","      <td>00:37.00</td>\n","      <td>Tutor</td>\n","      <td>If you want to type anything, you can use the ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef870776-b89a-417a-99c1-38d9bbab4120')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-ef870776-b89a-417a-99c1-38d9bbab4120 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-ef870776-b89a-417a-99c1-38d9bbab4120');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-be193d10-f3f5-424c-9f99-d0a51054f014\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be193d10-f3f5-424c-9f99-d0a51054f014')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-be193d10-f3f5-424c-9f99-d0a51054f014 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"text/plain":["      start      stop  speaker  \\\n","0  00:00.00  00:06.00    Tutor   \n","1  00:06.00  00:07.00  Student   \n","2  00:07.00  00:19.00    Tutor   \n","3  00:19.00  00:31.00    Tutor   \n","4  00:31.00  00:37.00    Tutor   \n","\n","                                            dialogue  \n","0  All right. Do you see the tools on the left-ha...  \n","1                                               Yes.  \n","2  All right. We're going to take a look at those...  \n","3  So the third button down is a pencil. If you c...  \n","4  If you want to type anything, you can use the ...  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["files = os.listdir(DATA_DIR)\n","df = utils.load_data(os.path.join(DATA_DIR, files[0]))\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"prh3yYoCU7pO"},"source":["There are two speakers: one is the Tutor and the other is the Student.\n","Let's defined these variables as well."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E7W9a3CdU7pO"},"outputs":[],"source":["STUDENT_SPEAKER = \"Student\"\n","TEACHER_SPEAKER = \"Tutor\""]},{"cell_type":"markdown","metadata":{"id":"200oT0QiU7pO"},"source":["## 📝 Text Pre-Processing and Annotation\n","\n","Let's first preprocess and annotate the dataset with `edu-convokit`. The following section will:\n","- Read each file in the dataset and preprocess it using `edu-convokit`'s `Preprocessor`.\n","- Then, annotate the file using `edu-convokit`'s `Annotator` for talktime, student reasoning and uptake.\n","- Finally, save the annotated file under `annotations/`.\n","\n","Let's get started!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["45fd1cdada9d4d568705fbaf1612f939","dff440afab09496386fca52de5cbf34d","6da68abc32024478b5271b461a45b0fd","d576b0f94f024ed9bdcb3a6ce4b6663f","fe2147cbb0104396bf1ca5bf3452a12b","3a345e70e21e473eadaf739e4ae6b6b2","76f612e8ceab4b249cbe3d1f252ec369","271efe0640a44a1ba245587bfbba7692","04302cacb6684c17b50fe628c9ada270","897f04cd16d14c0d8eed2d81a576ada1","3946c5555dce41fa849fe259889c7f5f","914099aa0aa2476ba99aef207dcc87e2","ffa4fcc9b8f2402aa6ecaf5439f6b988","810b8494f3554b92add1962b9e0277d4","9012a0b775a94faf9bd42257afcefad2","f90f765994454e25ad17e939049fa537","3370d1a72dad44d393ed8dbde15c0c6c","dc02af86a5754b51b18333aeff2a9803","7ae0887ae28d405e8b262ea670ea0185","9e6d4ad1012c4d7695f35d57281ea134","280c4aa8f738404cb1a99bc82a698d44","a310b77349844fdb9d1153091b2abccc","56393c2e58a449d7ab9c33c954b9197c","1b2e3e334af74d1a90dee0550c0f49a7","6ef85bd5b1ea4518890aea4391a702bf","0b288cb28cf74814ba5030d50bbc50fc","fa7e4cebe69845038bca5ae22ee514a8","6f6065f32dc74fef8b4c4e5c46e0ad58","3e0ddab423dd45b086bd121648f7d1d4","4d59944421d7456093fe04e4ebdc9281","03d242cea8904ca98401b4607197d67e","ad12defb83b247309129539f705b495f","cfef93ca7e7341d485c90fc3624e715d","7071b4d8b1a746f495dac399fcc6da02","4c234675926f48a5a26ac32809ab350e","6bca29edd8264e2bbc13811edd03cab9","a5896952b31747db99cd54786717cb10","1b347d51dae4407297e31cea2088c392","d077dd7cbacf4c01a814223c658d8207","4015bfb07670428baa7d1db1568f3b56","7f3e7aedda5f41faac9461c2c1d8a7ab","a38b337ed10a47b9b10abce3c82edb28","caeb491a36d54ca68efeec655bfe0971","3885e5f66b144f1087e5f61abfc2a6a3","8c575a73b7eb45739f06f14483370cd3","f6da7df34ee54288b02c6edc5ce8b355","729a92b0f2234ce497bf838d54657886","bcc80f4c7bf04d3c8662f281dbf9f8e6","7d05998550c447bba5aa09492f3523d2","cbd33cf9dae4418fa9fff03a783ec0e2","c2e76a2427804fb58f15e42487d4dbbc","c17edee81c6a41dfa12d9b8848ab17fd","d8809e84c7974536b1dbffe68cf4cdf9","add8754b6e534dc89d515a55bc64db4b","c6e6e29c298045fcb3a529d45dbbaa0d","649731e6f8a14d3a88c87c4741bccb89","0a4ff4ddaeeb4662ad53b7bbcd057ce0","38e4018a517f4368acb6f2d7ccff2eae","df6c1244aa4a43b9b6b5f4e3bfe2c58a","817b1d4046be4f8bab1ae3e6a3fac3dc","e655e7eef8514aa4886f31ad4aa99bfd","1bb8cb44b8b44974a557136728e2f920","af6259f2bca94016a9a0c906e1bba360","3a16603d01f3412aa06cf6b124d1ca6b","7af1122cbff641309c8a61f077d7e518","22f6061d882345c5914663f103b03b73","8aec33102c7d45458c087cff753618f7","f3423c392b60483e872c04c50ceb86b7","afe29d084c7849f58809b4fd1bcc7bd4","297dc3850b624721834e0a386cb5ed56","a402c2fdd233436db2795675077ca37b","12ccf60c027e462c958c8e0ebd5bfa46","634d1a160b0d44cd82f3d5ff025e3352","296fefe8cf3348088a7babdf8543d08b","6dea0d0a0a944a7fb62e4b476a592f21","80c9d8a91b904ca1ae55cd787d5f8bcd","657b93f272164e2588e7ed54deae2f00","77ac6e33ffdb448786500cac30e6c3b0","8147990c12b54912a02d466a9bddff0d","742a01e56a3d45ed9f1694f93655c6d4","ec1e34cf30c24ac78e1377f0e2b594a4","865f76923ce04f09800c9b474c520346","f378b38788364ca8847415ec566edc9e","192ec391928b48a489e7471bad35f39a","9a0bff32cd364149ae7c093e535d9ce5","661de037bf0343e49aa7a4f13186e33e","24691c46ac6d443aa5a6024fdfb6ca03","946a4e9939f54707b32be63e5042fa31","d41cd34ba50342278ffac687a0687806","e99afdc2c09348c19dd684f558cc3790","f7e59d3dfa2e4ba691c03c08291cac23","1d2141194a38429680fda1e38ec5475d","d2de477e20604f0eaa4201aa71a22496","1e8b5fc36f3f48bfad14d6b59191ba10","e3afba5c26044be98bc826bff19137ac","d8263580dc7c479c96208a32a61bf527","8018b4a1fc304d239f167ee5f3fdfdc9","ab0a48cdad0141d088dfaf14b98283fb","b5d7599700854dc786dfaf876cf34fa7","40452e5455464dcb81a6e0030cc05aa5","47d03aad1c6a4014b31ebfd1d2d112a6","63da77eedfec41779d693fc0ba315965","b67a73f9e2384b7589f4f5faa857000b","33efd1f5d7b7430e8cb2ba722b2bfb0f","2fb7b46f9f724f41b2c49d83c526857c","bc2716b115294c309db2355cc4539d4c","7283def6c3f14e5d9da63b6fc77c1532","37f87284163a44e696b4b5c3c46fb4d4","eb0ed2b1e22e4f539342b5e19f9b8423","14830f784b3440299964789d018559e1","2ae2534ed1464fbcb0f828d84a766702","9fdcee3740aa474f9375a2ca4bc014b2","dc8f63ad95034948b07537d4ac7f0f40","0ac300cad88949a8a1a884ee84af78cf","14ab4c6e2a5241ab996422463ec7020d","42f3646288e341aea39bb428b9c283f7","ef4c18f206624ff9a1d53c6c43883739","cdad23d9f384499ba5324ff4d32687d3","690f6f16aad34ddd87de595a362dd4df","c366e73ec2624a2fa488178ede0e795f","9b751b67808445cf94d6e6ceb1767ded"]},"executionInfo":{"elapsed":1142478,"status":"ok","timestamp":1703934444592,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"QwDwCJEMU7pO","outputId":"60be2b4b-0b39-4f7f-8bd6-bcf5a986e3f7"},"outputs":[{"name":"stderr","output_type":"stream","text":["\r  0%|          | 0/45 [00:00<?, ?it/s]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"45fd1cdada9d4d568705fbaf1612f939","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/429 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"914099aa0aa2476ba99aef207dcc87e2","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/238k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"56393c2e58a449d7ab9c33c954b9197c","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7071b4d8b1a746f495dac399fcc6da02","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/778 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8c575a73b7eb45739f06f14483370cd3","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"649731e6f8a14d3a88c87c4741bccb89","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8aec33102c7d45458c087cff753618f7","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"77ac6e33ffdb448786500cac30e6c3b0","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d41cd34ba50342278ffac687a0687806","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"40452e5455464dcb81a6e0030cc05aa5","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/585 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2ae2534ed1464fbcb0f828d84a766702","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["\r  2%|▏         | 1/45 [00:20<14:55, 20.36s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","  4%|▍         | 2/45 [00:58<22:12, 30.98s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","  7%|▋         | 3/45 [01:20<18:45, 26.79s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","  9%|▉         | 4/45 [01:52<19:38, 28.73s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 11%|█         | 5/45 [01:58<13:41, 20.53s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 13%|█▎        | 6/45 [02:25<14:42, 22.64s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 16%|█▌        | 7/45 [02:28<10:18, 16.29s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 18%|█▊        | 8/45 [03:00<13:15, 21.50s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 20%|██        | 9/45 [03:39<16:09, 26.93s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 22%|██▏       | 10/45 [03:43<11:30, 19.72s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 24%|██▍       | 11/45 [03:51<09:07, 16.10s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 27%|██▋       | 12/45 [03:54<06:43, 12.24s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 29%|██▉       | 13/45 [03:57<05:00,  9.40s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 31%|███       | 14/45 [04:09<05:18, 10.27s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 33%|███▎      | 15/45 [04:25<05:54, 11.82s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 36%|███▌      | 16/45 [04:46<07:09, 14.81s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 38%|███▊      | 17/45 [05:28<10:37, 22.77s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 40%|████      | 18/45 [06:30<15:38, 34.76s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 42%|████▏     | 19/45 [06:53<13:27, 31.06s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 44%|████▍     | 20/45 [07:20<12:29, 29.97s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 47%|████▋     | 21/45 [08:22<15:46, 39.42s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 49%|████▉     | 22/45 [08:44<13:10, 34.39s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 51%|█████     | 23/45 [08:51<09:30, 25.92s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 53%|█████▎    | 24/45 [09:26<10:05, 28.85s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 56%|█████▌    | 25/45 [10:05<10:34, 31.71s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 58%|█████▊    | 26/45 [10:26<09:03, 28.62s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 60%|██████    | 27/45 [10:30<06:21, 21.18s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 62%|██████▏   | 28/45 [11:01<06:49, 24.08s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 64%|██████▍   | 29/45 [12:14<10:19, 38.75s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 67%|██████▋   | 30/45 [12:41<08:50, 35.35s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 69%|██████▉   | 31/45 [13:56<11:01, 47.25s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 71%|███████   | 32/45 [14:21<08:46, 40.48s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 73%|███████▎  | 33/45 [14:24<05:50, 29.20s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 76%|███████▌  | 34/45 [14:38<04:33, 24.84s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 78%|███████▊  | 35/45 [15:15<04:42, 28.26s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 80%|████████  | 36/45 [15:52<04:40, 31.12s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 82%|████████▏ | 37/45 [16:12<03:40, 27.60s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 84%|████████▍ | 38/45 [16:35<03:04, 26.38s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 87%|████████▋ | 39/45 [16:53<02:22, 23.68s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 89%|████████▉ | 40/45 [16:55<01:27, 17.43s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 91%|█████████ | 41/45 [17:28<01:28, 22.06s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 93%|█████████▎| 42/45 [17:57<01:12, 24.07s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 96%|█████████▌| 43/45 [18:21<00:47, 23.87s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 98%|█████████▊| 44/45 [18:53<00:26, 26.48s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","100%|██████████| 45/45 [19:02<00:00, 25.38s/it]\n"]}],"source":["# Initialize the preprocessor and annotator\n","processor = TextPreprocessor()\n","annotator = Annotator()\n","\n","# This takes about 20 minutes on Colab, CPU\n","# Though this time varies depending on bandwidth\n","for filename in tqdm.tqdm(os.listdir(DATA_DIR)):\n","    if utils.is_valid_file_extension(filename):\n","        df = utils.load_data(os.path.join(DATA_DIR, filename))\n","\n","    # Preprocess the data. We're just going to merge the utterances of the same speaker together and directly update the dataframe.\n","    df = processor.merge_utterances_from_same_speaker(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        target_text_column=TEXT_COLUMN\n","    )\n","\n","    # Now we're going to annotate the data.\n","    df = annotator.get_talktime(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        output_column=TALK_TIME_COLUMN\n","    )\n","\n","    df = annotator.get_student_reasoning(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=STUDENT_REASONING_COLUMN,\n","        # We just want to annotate the student utterances. So we're going to specify the speaker value as STUDENT_SPEAKER.\n","        speaker_value=STUDENT_SPEAKER\n","    )\n","\n","    df = annotator.get_uptake(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=UPTAKE_COLUMN,\n","        # We want to annotate the teacher's uptake of the student's utterances.\n","        # So we're looking for instances where the student first speaks, then the teacher speaks.\n","        speaker1=STUDENT_SPEAKER,\n","        speaker2=TEACHER_SPEAKER\n","    )\n","\n","    # And we're done! Let's now save the annotated data as a csv file.\n","    filename = filename.split(\".\")[0] + \".csv\"\n","    df.to_csv(os.path.join(ANNOTATIONS_DIR, filename), index=False)"]},{"cell_type":"markdown","metadata":{"id":"wj2tx1weU7pP"},"source":["## Analysis\n","\n","Now that we have annotated the dataset, let's analyze it using `edu-convokit`'s `Analyzer`. We'll be doing the following:\n","- We'll use `QualitativeAnalyzer` to look at some examples of the talktime, student reasoning and uptake annotations.\n","- We'll use `QuantitativeAnalyzer` to look at the aggregate statistics of the talktime, student reasoning and uptake annotations.\n","- We'll use `LexicalAnalyzer` to compare the student and tutor's vocabulary.\n","- We'll use `TemporalAnalyzer` to look at the temporal trends of the talktime, student reasoning and uptake annotations.\n","\n","Let's get started!!!"]},{"cell_type":"markdown","metadata":{"id":"TVT7G6N6U7pP"},"source":["## 🔍 Qualitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":495,"status":"ok","timestamp":1703934557558,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"F6Kav6GcU7pP","outputId":"c4e4d805-97ca-4212-f47e-88be8f0fe9ad"},"outputs":[{"name":"stdout","output_type":"stream","text":["talktime: 34\n",">> Tutor: Okay, we're going to be talking back and forth like this, so you can close your chat box if you would like, and that'll open up your screen so you have more work space.\n","\n","talktime: 34\n",">> Tutor: So I understand what you're thinking. Don't worry about changing those dots. What is? where are you? Though, most likely that's causing you to be in stop and go traffic from D to E.\n","\n","talktime: 34\n",">> Tutor: We're going to look at just those three sections, A to b, B to c, C to D, and just those three sections of the graph. I'd like you to sketch a new graph.\n","\n","talktime: 1\n",">> Student: Then.\n","\n","talktime: 1\n",">> Student: At.\n","\n","talktime: 1\n",">> Student: But\n","\n","talktime: 11\n",">> Tutor: On the lefthand side of your screen, you see some tools.\n","\n","talktime: 11\n",">> Student: The distance by hour, like how far they're moving per hour.\n","\n","talktime: 11\n",">> Tutor: Is that point a a break? Is that what you just?\n","\n"]}],"source":["# We're going to look at examples from the entire dataset.\n","qualitative_analyzer = QualitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Examples of talktime. Will show random examples from the dataset.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=TALK_TIME_COLUMN,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":113,"status":"ok","timestamp":1703934560717,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"6cPvJpRlU7pP","outputId":"939c1d0d-a27d-47c0-c63e-fa5da90479cf"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 1.0\n",">> Student: Um, maybe like they're measuring how far or how long it would take to get from Providence to Newark.\n","\n","student_reasoning: 1.0\n",">> Student: Uh, it's similar'cause the two different units I was talking about miles and hours and they're measuring the distance.\n","\n","student_reasoning: 1.0\n",">> Student: I'll just say A lo, Um, from A to B. it's increasing, and from B to C. it's staying the same from C to D. It's increasing from Dt. it's increasing.\n","\n"]}],"source":["# Examples of student reasoning. Let's look at positive examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=1.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703934561100,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"UZHe7HaRU7pP","outputId":"7772f8f0-acbf-4519-a52e-dff6b1f8d5a5"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 0.0\n",">> Student: Um, how many miles per hour the car is driving?\n","\n","student_reasoning: 0.0\n",">> Student: The distance by hour, like how far they're moving per hour.\n","\n","student_reasoning: 0.0\n",">> Student: Uh, the mileage I think, is it? Ah, I do't.\n","\n"]}],"source":["# We can also look at negative examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=0.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":151,"status":"ok","timestamp":1703934562146,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"v57F3ljGU7pP","outputId":"2b19ff97-a424-4ed4-db8a-4f7dd9bc3865"},"outputs":[{"name":"stdout","output_type":"stream","text":["uptake: 1.0\n","Student: Um, maybe like they're measuring how far or how long it would take to get from Providence to Newark.\n",">> Tutor: Cool if they're measuring how far or how long what units would they be using to?\n","\n","uptake: 1.0\n","Student: Um, how many miles per hour the car is driving?\n",">> Tutor: Wo mile per hour be keeping track of.\n","\n","uptake: 1.0\n","Student: The distance by hour, like how far they're moving per hour.\n",">> Tutor: Ah, and do you know how else we can describe that? What's that thing that we're talking about with the car? if we're describing how far per hour it's going?\n","\n"]}],"source":["# Examples of uptake.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=UPTAKE_COLUMN,\n","    # I want to look at positive examples of uptake (uptake = 1.0)\n","    feature_value=1.0,\n","    # ... and look at the previous student utterance (show_k_previous_lines = 1).\n","    # This is interesting because it will show us how the teacher is responding to the student's utterance.\n","    show_k_previous_lines=1,\n",")"]},{"cell_type":"markdown","metadata":{"id":"ofu3QH2MU7pP"},"source":["## 📊 Quantitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":616},"executionInfo":{"elapsed":983,"status":"ok","timestamp":1703934564429,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"UkqO4dmnU7pP","outputId":"4595a62a-c0af-4dd5-8d1d-4c1bb8fcfd0d"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["talktime\n","\n","Proportion statistics\n","         count      mean       std       min       25%       50%       75%       max\n","speaker                                                                             \n","None       2.0  0.014643  0.012308  0.005940  0.010291  0.014643  0.018995  0.023346\n","Student   44.0  0.304822  0.213351  0.009091  0.159112  0.272535  0.367901  0.955194\n","Tutor     45.0  0.701301  0.215427  0.044806  0.631480  0.727766  0.840467  1.000000\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["quantitative_analyzer = QuantitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Let's plot the talk time ratio between the speakers.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\"\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":747},"executionInfo":{"elapsed":941,"status":"ok","timestamp":1703934566294,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"hzLvzhHyU7pP","outputId":"b9925a5d-e14e-4a76-e824-0dd73e8e9a2a"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["student_reasoning\n","\n","Average statistics\n","         count      mean       std  min       25%       50%       75%       max\n","speaker                                                                        \n","None       0.0       NaN       NaN  NaN       NaN       NaN       NaN       NaN\n","Student   38.0  0.154413  0.114317  0.0  0.056493  0.158859  0.238824  0.388889\n","Tutor      0.0       NaN       NaN  NaN       NaN       NaN       NaN       NaN\n","\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# What about the student reasoning? How often does the student use reasoning?\n","quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We change this to \"avg\" because we're now looking at within-speaker statistics.\n","    value_as=\"avg\",\n","    # We can set the y-axis limits to [0, 1] because the student reasoning column is a binary column.\n","    yrange=(0, 1)\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\"\n",")"]},{"cell_type":"markdown","metadata":{"id":"wD__EVCMU7pP"},"source":["Note, the tutor has no `student_reasoning` because we did not annotate the tutor's utterances for student reasoning.\n","We can easily remove the tutor from the plot by dropping na values:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":713},"executionInfo":{"elapsed":526,"status":"ok","timestamp":1703934568763,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"xop-U7CCU7pP","outputId":"d7786cd8-2132-4062-e4ac-354af6517101"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":539},"executionInfo":{"elapsed":603,"status":"ok","timestamp":1703934570319,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"7hFL7VpbU7pP","outputId":"ba98b1b3-12aa-455c-ca8f-221da59725f5"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"km5QAOzXU7pQ"},"source":["## 💬 Lexical Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21217,"status":"ok","timestamp":1703934592486,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"aWDhIodqU7pQ","outputId":"568b5046-d072-42d1-85f1-f9f64da10c7c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Top Words By Speaker\n","Tutor\n","going: 942\n","miles: 929\n","okay: 810\n","like: 789\n","speed: 727\n","hour: 715\n","time: 711\n","car: 668\n","yeah: 620\n","graph: 579\n","\n","\n","Student\n","like: 1367\n","miles: 780\n","hour: 660\n","yeah: 564\n","would: 474\n","okay: 434\n","fifty: 431\n","point: 428\n","distance: 423\n","think: 411\n","\n","\n","None\n","okay: 4\n","cool: 1\n","yeah: 1\n","average: 1\n","make: 1\n","sense: 1\n","yep: 1\n","three: 1\n","four: 1\n","yes: 1\n","\n","\n","\n"]}],"source":["lexical_analyzer = LexicalAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Let's look at the most common words per speaker in the dataset.\n","lexical_analyzer.print_word_frequency(\n","    text_column=TEXT_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We want to look at the top 10 words per speaker.\n","    topk=10,\n","    # Let's also format the text (e.g., remove punctuation, lowercase the text, etc.)\n","    run_text_formatting=True\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"h3z4Ude0U7pQ"},"source":["The language between the two looks pretty similar. Let's run a log-odds analysis to see what words that are more likely to be used by the tutor vs. student."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":516},"executionInfo":{"elapsed":25035,"status":"ok","timestamp":1703934620837,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"0RC6oPiqU7pQ","outputId":"97365083-56bd-42de-f467-9b9faf5ee4e9"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# This returns the merged dataframe of the annotated files in DATA_DIR.\n","df = lexical_analyzer.get_df()\n","\n","# We want to create two groups of df: one for the student and one for the tutor.\n","student_df = df[df[SPEAKER_COLUMN] == STUDENT_SPEAKER]\n","tutor_df = df[df[SPEAKER_COLUMN] == TEACHER_SPEAKER]\n","\n","# Now we can run the log-odds analysis:\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=tutor_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    # Let's name the df groups to show on the plot\n","    group1_name=\"Student\",\n","    group2_name=\"Tutor\",\n","    # Let's also run the text formatting\n","    run_text_formatting=True,\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":901},"executionInfo":{"elapsed":22211,"status":"ok","timestamp":1703934643046,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"jMU0BbqDU7pQ","outputId":"00fdf8ca-cace-4889-cfa5-4379887a3b08"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# We might also be interested in other n-grams. Let's look at the top 10 bigrams per speaker.\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=tutor_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    group1_name=\"Student\",\n","    group2_name=\"Tutor\",\n","    run_text_formatting=True,\n","    # n-grams:\n","    run_ngrams=True,\n","    n=2,\n","    topk=10\n",")"]},{"cell_type":"markdown","metadata":{"id":"Lah3VQtzU7pQ"},"source":["## 📈 Temporal Analysis\n","\n","Let's look at the temporal trends of the talktime, student reasoning and uptake annotations!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":477},"executionInfo":{"elapsed":1993,"status":"ok","timestamp":1703934648407,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"pFvHDA8qU7pQ","outputId":"6c52f123-667a-4a3f-c37e-f2feac7de343"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["temporal_analyzer = TemporalAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# First let's look at the talk time ratio between the speakers over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"prop\",\n","    # Let's create 10 bins for the x-axis.\n","    num_bins=10\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":1546,"status":"ok","timestamp":1703934657800,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"-JTOzSNtU7pQ","outputId":"29c78db0-f8df-4d6b-abf9-dae0dd5d21ab"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Now student reasoning over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 10 bins for the x-axis.\n","    num_bins=10,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":2268,"status":"ok","timestamp":1703934664052,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"Tmw7Db-ZU7pQ","outputId":"d6a07916-840b-409b-dc0b-b8391007fb1b"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 10 bins for the x-axis.\n","    num_bins=10,\n",")"]},{"cell_type":"markdown","metadata":{"id":"sd26pOs-U7pQ"},"source":["## 📚 Conclusions and Next Steps\n","\n","Great! From this tutorial, we learned how to use `edu-convokit` to preprocess, annotate and analyze the Amber dataset.\n","We saw how very simple principles built into `edu-convokit` can be used to analyze the dataset and gain insights into the data from various perspectives (qualitative, quantitative, lexical and temporal).\n","\n","Other resources you can check out include:\n","- [Tutorial on edu-convokit for the NCTE dataset](https://colab.research.google.com/drive/1k3fn6uY4QRMtPUZN6hpMd6o-0g7fYotg)\n","- [Tutorial on edu-convokit for the TalkMoves dataset](https://colab.research.google.com/drive/1qt_S3GjxIwXk6ONztbYAHeX8WHy1uxDd)\n","- [`edu-convokit` documentation](https://edu-convokit.readthedocs.io/en/latest/)\n","- [`edu-convokit` GitHub repository](https://github.com/rosewang2008/edu-convokit/tree/main)\n","\n","If you have any questions, please feel free to reach out to us on [`edu-convokit`'s GitHub](https://github.com/rosewang2008/edu-convokit).\n","\n","👋 Happy exploring your data with `edu-convokit`!"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"},"widgets":{"application/vnd.jupyter.widget-state+json":{"03d242cea8904ca98401b4607197d67e":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"04302cacb6684c17b50fe628c9ada270":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"0a4ff4ddaeeb4662ad53b7bbcd057ce0":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_e655e7eef8514aa4886f31ad4aa99bfd","placeholder":"​","style":"IPY_MODEL_1bb8cb44b8b44974a557136728e2f920","value":"tokenizer_config.json: 100%"}},"0ac300cad88949a8a1a884ee84af78cf":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_c366e73ec2624a2fa488178ede0e795f","placeholder":"​","style":"IPY_MODEL_9b751b67808445cf94d6e6ceb1767ded","value":" 438M/438M [00:06&lt;00:00, 51.3MB/s]"}},"0b288cb28cf74814ba5030d50bbc50fc":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_ad12defb83b247309129539f705b495f","placeholder":"​","style":"IPY_MODEL_cfef93ca7e7341d485c90fc3624e715d","value":" 124/124 [00:00&lt;00:00, 5.30kB/s]"}},"12ccf60c027e462c958c8e0ebd5bfa46":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"14830f784b3440299964789d018559e1":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"14ab4c6e2a5241ab996422463ec7020d":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"192ec391928b48a489e7471bad35f39a":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"1b2e3e334af74d1a90dee0550c0f49a7":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_6f6065f32dc74fef8b4c4e5c46e0ad58","placeholder":"​","style":"IPY_MODEL_3e0ddab423dd45b086bd121648f7d1d4","value":"special_tokens_map.json: 100%"}},"1b347d51dae4407297e31cea2088c392":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"1bb8cb44b8b44974a557136728e2f920":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"1d2141194a38429680fda1e38ec5475d":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_ab0a48cdad0141d088dfaf14b98283fb","placeholder":"​","style":"IPY_MODEL_b5d7599700854dc786dfaf876cf34fa7","value":" 570/570 [00:00&lt;00:00, 29.5kB/s]"}},"1e8b5fc36f3f48bfad14d6b59191ba10":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"22f6061d882345c5914663f103b03b73":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"24691c46ac6d443aa5a6024fdfb6ca03":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"271efe0640a44a1ba245587bfbba7692":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"280c4aa8f738404cb1a99bc82a698d44":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"296fefe8cf3348088a7babdf8543d08b":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"297dc3850b624721834e0a386cb5ed56":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_80c9d8a91b904ca1ae55cd787d5f8bcd","placeholder":"​","style":"IPY_MODEL_657b93f272164e2588e7ed54deae2f00","value":" 232k/232k [00:00&lt;00:00, 5.82MB/s]"}},"2ae2534ed1464fbcb0f828d84a766702":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_9fdcee3740aa474f9375a2ca4bc014b2","IPY_MODEL_dc8f63ad95034948b07537d4ac7f0f40","IPY_MODEL_0ac300cad88949a8a1a884ee84af78cf"],"layout":"IPY_MODEL_14ab4c6e2a5241ab996422463ec7020d"}},"2fb7b46f9f724f41b2c49d83c526857c":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"3370d1a72dad44d393ed8dbde15c0c6c":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"33efd1f5d7b7430e8cb2ba722b2bfb0f":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"37f87284163a44e696b4b5c3c46fb4d4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"3885e5f66b144f1087e5f61abfc2a6a3":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"38e4018a517f4368acb6f2d7ccff2eae":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_af6259f2bca94016a9a0c906e1bba360","max":28,"min":0,"orientation":"horizontal","style":"IPY_MODEL_3a16603d01f3412aa06cf6b124d1ca6b","value":28}},"3946c5555dce41fa849fe259889c7f5f":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"3a16603d01f3412aa06cf6b124d1ca6b":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"3a345e70e21e473eadaf739e4ae6b6b2":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"3e0ddab423dd45b086bd121648f7d1d4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"4015bfb07670428baa7d1db1568f3b56":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"40452e5455464dcb81a6e0030cc05aa5":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_47d03aad1c6a4014b31ebfd1d2d112a6","IPY_MODEL_63da77eedfec41779d693fc0ba315965","IPY_MODEL_b67a73f9e2384b7589f4f5faa857000b"],"layout":"IPY_MODEL_33efd1f5d7b7430e8cb2ba722b2bfb0f"}},"42f3646288e341aea39bb428b9c283f7":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"45fd1cdada9d4d568705fbaf1612f939":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_dff440afab09496386fca52de5cbf34d","IPY_MODEL_6da68abc32024478b5271b461a45b0fd","IPY_MODEL_d576b0f94f024ed9bdcb3a6ce4b6663f"],"layout":"IPY_MODEL_fe2147cbb0104396bf1ca5bf3452a12b"}},"47d03aad1c6a4014b31ebfd1d2d112a6":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_2fb7b46f9f724f41b2c49d83c526857c","placeholder":"​","style":"IPY_MODEL_bc2716b115294c309db2355cc4539d4c","value":"config.json: 100%"}},"4c234675926f48a5a26ac32809ab350e":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_d077dd7cbacf4c01a814223c658d8207","placeholder":"​","style":"IPY_MODEL_4015bfb07670428baa7d1db1568f3b56","value":"config.json: 100%"}},"4d59944421d7456093fe04e4ebdc9281":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"56393c2e58a449d7ab9c33c954b9197c":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_1b2e3e334af74d1a90dee0550c0f49a7","IPY_MODEL_6ef85bd5b1ea4518890aea4391a702bf","IPY_MODEL_0b288cb28cf74814ba5030d50bbc50fc"],"layout":"IPY_MODEL_fa7e4cebe69845038bca5ae22ee514a8"}},"634d1a160b0d44cd82f3d5ff025e3352":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"63da77eedfec41779d693fc0ba315965":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_7283def6c3f14e5d9da63b6fc77c1532","max":585,"min":0,"orientation":"horizontal","style":"IPY_MODEL_37f87284163a44e696b4b5c3c46fb4d4","value":585}},"649731e6f8a14d3a88c87c4741bccb89":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_0a4ff4ddaeeb4662ad53b7bbcd057ce0","IPY_MODEL_38e4018a517f4368acb6f2d7ccff2eae","IPY_MODEL_df6c1244aa4a43b9b6b5f4e3bfe2c58a"],"layout":"IPY_MODEL_817b1d4046be4f8bab1ae3e6a3fac3dc"}},"657b93f272164e2588e7ed54deae2f00":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"661de037bf0343e49aa7a4f13186e33e":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"690f6f16aad34ddd87de595a362dd4df":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"6bca29edd8264e2bbc13811edd03cab9":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_7f3e7aedda5f41faac9461c2c1d8a7ab","max":778,"min":0,"orientation":"horizontal","style":"IPY_MODEL_a38b337ed10a47b9b10abce3c82edb28","value":778}},"6da68abc32024478b5271b461a45b0fd":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_271efe0640a44a1ba245587bfbba7692","max":429,"min":0,"orientation":"horizontal","style":"IPY_MODEL_04302cacb6684c17b50fe628c9ada270","value":429}},"6dea0d0a0a944a7fb62e4b476a592f21":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"6ef85bd5b1ea4518890aea4391a702bf":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_4d59944421d7456093fe04e4ebdc9281","max":124,"min":0,"orientation":"horizontal","style":"IPY_MODEL_03d242cea8904ca98401b4607197d67e","value":124}},"6f6065f32dc74fef8b4c4e5c46e0ad58":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"7071b4d8b1a746f495dac399fcc6da02":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_4c234675926f48a5a26ac32809ab350e","IPY_MODEL_6bca29edd8264e2bbc13811edd03cab9","IPY_MODEL_a5896952b31747db99cd54786717cb10"],"layout":"IPY_MODEL_1b347d51dae4407297e31cea2088c392"}},"7283def6c3f14e5d9da63b6fc77c1532":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"729a92b0f2234ce497bf838d54657886":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_c17edee81c6a41dfa12d9b8848ab17fd","max":438007925,"min":0,"orientation":"horizontal","style":"IPY_MODEL_d8809e84c7974536b1dbffe68cf4cdf9","value":438007925}},"742a01e56a3d45ed9f1694f93655c6d4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_9a0bff32cd364149ae7c093e535d9ce5","max":466062,"min":0,"orientation":"horizontal","style":"IPY_MODEL_661de037bf0343e49aa7a4f13186e33e","value":466062}},"76f612e8ceab4b249cbe3d1f252ec369":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"77ac6e33ffdb448786500cac30e6c3b0":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_8147990c12b54912a02d466a9bddff0d","IPY_MODEL_742a01e56a3d45ed9f1694f93655c6d4","IPY_MODEL_ec1e34cf30c24ac78e1377f0e2b594a4"],"layout":"IPY_MODEL_865f76923ce04f09800c9b474c520346"}},"7ae0887ae28d405e8b262ea670ea0185":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"7af1122cbff641309c8a61f077d7e518":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"7d05998550c447bba5aa09492f3523d2":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"7f3e7aedda5f41faac9461c2c1d8a7ab":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"8018b4a1fc304d239f167ee5f3fdfdc9":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"80c9d8a91b904ca1ae55cd787d5f8bcd":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"810b8494f3554b92add1962b9e0277d4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_7ae0887ae28d405e8b262ea670ea0185","max":237661,"min":0,"orientation":"horizontal","style":"IPY_MODEL_9e6d4ad1012c4d7695f35d57281ea134","value":237661}},"8147990c12b54912a02d466a9bddff0d":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_f378b38788364ca8847415ec566edc9e","placeholder":"​","style":"IPY_MODEL_192ec391928b48a489e7471bad35f39a","value":"tokenizer.json: 100%"}},"817b1d4046be4f8bab1ae3e6a3fac3dc":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"865f76923ce04f09800c9b474c520346":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"897f04cd16d14c0d8eed2d81a576ada1":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"8aec33102c7d45458c087cff753618f7":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_f3423c392b60483e872c04c50ceb86b7","IPY_MODEL_afe29d084c7849f58809b4fd1bcc7bd4","IPY_MODEL_297dc3850b624721834e0a386cb5ed56"],"layout":"IPY_MODEL_a402c2fdd233436db2795675077ca37b"}},"8c575a73b7eb45739f06f14483370cd3":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_f6da7df34ee54288b02c6edc5ce8b355","IPY_MODEL_729a92b0f2234ce497bf838d54657886","IPY_MODEL_bcc80f4c7bf04d3c8662f281dbf9f8e6"],"layout":"IPY_MODEL_7d05998550c447bba5aa09492f3523d2"}},"9012a0b775a94faf9bd42257afcefad2":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_280c4aa8f738404cb1a99bc82a698d44","placeholder":"​","style":"IPY_MODEL_a310b77349844fdb9d1153091b2abccc","value":" 238k/238k [00:00&lt;00:00, 4.39MB/s]"}},"914099aa0aa2476ba99aef207dcc87e2":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_ffa4fcc9b8f2402aa6ecaf5439f6b988","IPY_MODEL_810b8494f3554b92add1962b9e0277d4","IPY_MODEL_9012a0b775a94faf9bd42257afcefad2"],"layout":"IPY_MODEL_f90f765994454e25ad17e939049fa537"}},"946a4e9939f54707b32be63e5042fa31":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"9a0bff32cd364149ae7c093e535d9ce5":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"9b751b67808445cf94d6e6ceb1767ded":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"9e6d4ad1012c4d7695f35d57281ea134":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"9fdcee3740aa474f9375a2ca4bc014b2":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_42f3646288e341aea39bb428b9c283f7","placeholder":"​","style":"IPY_MODEL_ef4c18f206624ff9a1d53c6c43883739","value":"pytorch_model.bin: 100%"}},"a310b77349844fdb9d1153091b2abccc":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"a38b337ed10a47b9b10abce3c82edb28":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"a402c2fdd233436db2795675077ca37b":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"a5896952b31747db99cd54786717cb10":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_caeb491a36d54ca68efeec655bfe0971","placeholder":"​","style":"IPY_MODEL_3885e5f66b144f1087e5f61abfc2a6a3","value":" 778/778 [00:00&lt;00:00, 33.2kB/s]"}},"ab0a48cdad0141d088dfaf14b98283fb":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"ad12defb83b247309129539f705b495f":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"add8754b6e534dc89d515a55bc64db4b":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"af6259f2bca94016a9a0c906e1bba360":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"afe29d084c7849f58809b4fd1bcc7bd4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_296fefe8cf3348088a7babdf8543d08b","max":231508,"min":0,"orientation":"horizontal","style":"IPY_MODEL_6dea0d0a0a944a7fb62e4b476a592f21","value":231508}},"b5d7599700854dc786dfaf876cf34fa7":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"b67a73f9e2384b7589f4f5faa857000b":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_eb0ed2b1e22e4f539342b5e19f9b8423","placeholder":"​","style":"IPY_MODEL_14830f784b3440299964789d018559e1","value":" 585/585 [00:00&lt;00:00, 20.8kB/s]"}},"bc2716b115294c309db2355cc4539d4c":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"bcc80f4c7bf04d3c8662f281dbf9f8e6":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_add8754b6e534dc89d515a55bc64db4b","placeholder":"​","style":"IPY_MODEL_c6e6e29c298045fcb3a529d45dbbaa0d","value":" 438M/438M [00:06&lt;00:00, 63.9MB/s]"}},"c17edee81c6a41dfa12d9b8848ab17fd":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"c2e76a2427804fb58f15e42487d4dbbc":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"c366e73ec2624a2fa488178ede0e795f":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"c6e6e29c298045fcb3a529d45dbbaa0d":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"caeb491a36d54ca68efeec655bfe0971":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"cbd33cf9dae4418fa9fff03a783ec0e2":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"cdad23d9f384499ba5324ff4d32687d3":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"cfef93ca7e7341d485c90fc3624e715d":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"d077dd7cbacf4c01a814223c658d8207":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d2de477e20604f0eaa4201aa71a22496":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d41cd34ba50342278ffac687a0687806":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_e99afdc2c09348c19dd684f558cc3790","IPY_MODEL_f7e59d3dfa2e4ba691c03c08291cac23","IPY_MODEL_1d2141194a38429680fda1e38ec5475d"],"layout":"IPY_MODEL_d2de477e20604f0eaa4201aa71a22496"}},"d576b0f94f024ed9bdcb3a6ce4b6663f":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_897f04cd16d14c0d8eed2d81a576ada1","placeholder":"​","style":"IPY_MODEL_3946c5555dce41fa849fe259889c7f5f","value":" 429/429 [00:00&lt;00:00, 19.8kB/s]"}},"d8263580dc7c479c96208a32a61bf527":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d8809e84c7974536b1dbffe68cf4cdf9":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"ProgressStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"dc02af86a5754b51b18333aeff2a9803":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"dc8f63ad95034948b07537d4ac7f0f40":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_cdad23d9f384499ba5324ff4d32687d3","max":437989703,"min":0,"orientation":"horizontal","style":"IPY_MODEL_690f6f16aad34ddd87de595a362dd4df","value":437989703}},"df6c1244aa4a43b9b6b5f4e3bfe2c58a":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_7af1122cbff641309c8a61f077d7e518","placeholder":"​","style":"IPY_MODEL_22f6061d882345c5914663f103b03b73","value":" 28.0/28.0 [00:00&lt;00:00, 1.03kB/s]"}},"dff440afab09496386fca52de5cbf34d":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_3a345e70e21e473eadaf739e4ae6b6b2","placeholder":"​","style":"IPY_MODEL_76f612e8ceab4b249cbe3d1f252ec369","value":"tokenizer_config.json: 100%"}},"e3afba5c26044be98bc826bff19137ac":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"e655e7eef8514aa4886f31ad4aa99bfd":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"e99afdc2c09348c19dd684f558cc3790":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_1e8b5fc36f3f48bfad14d6b59191ba10","placeholder":"​","style":"IPY_MODEL_e3afba5c26044be98bc826bff19137ac","value":"config.json: 100%"}},"eb0ed2b1e22e4f539342b5e19f9b8423":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"ec1e34cf30c24ac78e1377f0e2b594a4":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_24691c46ac6d443aa5a6024fdfb6ca03","placeholder":"​","style":"IPY_MODEL_946a4e9939f54707b32be63e5042fa31","value":" 466k/466k [00:00&lt;00:00, 19.2MB/s]"}},"ef4c18f206624ff9a1d53c6c43883739":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"f3423c392b60483e872c04c50ceb86b7":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_12ccf60c027e462c958c8e0ebd5bfa46","placeholder":"​","style":"IPY_MODEL_634d1a160b0d44cd82f3d5ff025e3352","value":"vocab.txt: 100%"}},"f378b38788364ca8847415ec566edc9e":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"f6da7df34ee54288b02c6edc5ce8b355":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_cbd33cf9dae4418fa9fff03a783ec0e2","placeholder":"​","style":"IPY_MODEL_c2e76a2427804fb58f15e42487d4dbbc","value":"pytorch_model.bin: 100%"}},"f7e59d3dfa2e4ba691c03c08291cac23":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_d8263580dc7c479c96208a32a61bf527","max":570,"min":0,"orientation":"horizontal","style":"IPY_MODEL_8018b4a1fc304d239f167ee5f3fdfdc9","value":570}},"f90f765994454e25ad17e939049fa537":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"fa7e4cebe69845038bca5ae22ee514a8":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"fe2147cbb0104396bf1ca5bf3452a12b":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"ffa4fcc9b8f2402aa6ecaf5439f6b988":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HTMLModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_3370d1a72dad44d393ed8dbde15c0c6c","placeholder":"​","style":"IPY_MODEL_dc02af86a5754b51b18333aeff2a9803","value":"vocab.txt: 100%"}}}}},"nbformat":4,"nbformat_minor":0}