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"state": {
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"_model_module_version": "1.5.0",
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"_model_name": "DescriptionStyleModel",
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||
},
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Download CookDial from git"
|
||
],
|
||
"metadata": {
|
||
"id": "PXzanHU0dk5B"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"! git clone https://github.com/YiweiJiang2015/CookDial.git"
|
||
],
|
||
"metadata": {
|
||
"id": "7WD7dXWLdohP"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Or download CookDial from Google Drive"
|
||
],
|
||
"metadata": {
|
||
"id": "JcGaYbYZbBiM"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "a7gr9prdwinA",
|
||
"outputId": "f3e595eb-0755-47f5-eb34-ba6638fa3450"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Mounted at /content/drive\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load the Drive helper and mount\n",
|
||
"from google.colab import drive\n",
|
||
"drive.mount('/content/drive')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"path_to_file = \"/content/CookDial\"\n",
|
||
"path_to_output = \"/content/drive/MyDrive/CookDial\""
|
||
],
|
||
"metadata": {
|
||
"id": "n1Gxyyd6wySX"
|
||
},
|
||
"execution_count": 3,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## CookDial from MyDrive"
|
||
],
|
||
"metadata": {
|
||
"id": "KpwW6KpjdvWs"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import zipfile\n",
|
||
"with zipfile.ZipFile(path_to_output + \".zip\",\"r\") as zip_ref:\n",
|
||
" zip_ref.extractall(path_to_file)"
|
||
],
|
||
"metadata": {
|
||
"id": "_wtHpzYcwzE2"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## CookDial to MyDrive"
|
||
],
|
||
"metadata": {
|
||
"id": "J1wZS4UQd12C"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import shutil\n",
|
||
"\n",
|
||
"shutil.make_archive(path_to_output, 'zip', path_to_file)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 35
|
||
},
|
||
"id": "K-xDfMvidcNK",
|
||
"outputId": "ff3aa1b3-443e-4daf-abbe-cb870501ccd0"
|
||
},
|
||
"execution_count": 71,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"'/content/drive/MyDrive/CookDial.zip'"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "string"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 71
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Get dialogues number"
|
||
],
|
||
"metadata": {
|
||
"id": "cF2OEPch0w77"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import os\n",
|
||
"APP_FOLDER = '/content/CookDial/data/dialog'\n",
|
||
"totalFiles = 0\n",
|
||
"for base, _, files in os.walk(APP_FOLDER):\n",
|
||
" print('Searching in : ',base)\n",
|
||
" for File in files:\n",
|
||
" totalFiles += 1\n",
|
||
"\n",
|
||
"print('Total number of files',totalFiles)\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "LXmFpKPdyMBL",
|
||
"outputId": "57aa1d85-1f64-4ed1-f40e-0bb8f858c751"
|
||
},
|
||
"execution_count": 5,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Searching in : /content/CookDial/data/dialog\n",
|
||
"Total number of files 260\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Read content of data"
|
||
],
|
||
"metadata": {
|
||
"id": "hC8lWFZ93puq"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import re \n",
|
||
"\n",
|
||
"pattern = re.compile(r\"\\\"intent\\\": \\\"([^\\\"]*)\", re.IGNORECASE)\n",
|
||
"\n",
|
||
"def parse_annotation(annotation):\n",
|
||
" # print(annotation)\n",
|
||
" result = re.search(pattern, annotation)\n",
|
||
" value = result.group(1)\n",
|
||
" value = value.replace(\";\", \"\")\n",
|
||
" value = value.replace(\" \", \"#\")\n",
|
||
" return value"
|
||
],
|
||
"metadata": {
|
||
"id": "FOhsU_mT7DIh"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import json\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"utt_dict = {'label': [], 'sentence': []}\n",
|
||
"\n",
|
||
"for number in range(totalFiles):\n",
|
||
" with open(APP_FOLDER + \"/\" + f\"{number:03d}\" + \".1.json\") as f:\n",
|
||
"\n",
|
||
" data = json.load(f)\n",
|
||
" for row in data['messages']:\n",
|
||
" if False == row[\"bot\"]:\n",
|
||
" parsed_ann = parse_annotation(row[\"annotations\"])\n",
|
||
" if \"\" != parsed_ann:\n",
|
||
" utt_dict[\"label\"].append(parsed_ann)\n",
|
||
" utt_dict[\"sentence\"].append(row[\"utterance\"].lower())\n",
|
||
"\n",
|
||
"IntentDataFrame = pd.DataFrame(utt_dict)"
|
||
],
|
||
"metadata": {
|
||
"id": "GVRy6RzT3pWz"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"IntentDataFrame.sample(n=5)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 206
|
||
},
|
||
"id": "Kx_SQpsG--jO",
|
||
"outputId": "5c975894-b980-423f-a530-bdcbb66c2819"
|
||
},
|
||
"execution_count": 43,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" label sentence\n",
|
||
"2260 10 ok good! i am ready to start now.\n",
|
||
"4431 17 great. the chicken are added back. can i eat it?\n",
|
||
"4425 19 ok. done. how long to wait from now?\n",
|
||
"2246 29 ok nice, for how long should they cook?\n",
|
||
"141 5 ok. i have added the baking powder to the bowl."
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-abbc2732-9289-4ea7-9ff8-fcf63bab7eb5\">\n",
|
||
" <div 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>label</th>\n",
|
||
" <th>sentence</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2260</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>ok good! i am ready to start now.</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4431</th>\n",
|
||
" <td>17</td>\n",
|
||
" <td>great. the chicken are added back. can i eat it?</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4425</th>\n",
|
||
" <td>19</td>\n",
|
||
" <td>ok. done. how long to wait from now?</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2246</th>\n",
|
||
" <td>29</td>\n",
|
||
" <td>ok nice, for how long should they cook?</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>141</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>ok. i have added the baking powder to the bowl.</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abbc2732-9289-4ea7-9ff8-fcf63bab7eb5')\"\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 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
|
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" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
" \n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" flex-wrap:wrap;\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",
|
||
" [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-abbc2732-9289-4ea7-9ff8-fcf63bab7eb5 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-abbc2732-9289-4ea7-9ff8-fcf63bab7eb5');\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",
|
||
" </div>\n",
|
||
" "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 43
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"print(\"There are {} rows and {} columns\".format(IntentDataFrame.shape[0], IntentDataFrame.shape[1]))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "JQTPn9LlGyso",
|
||
"outputId": "4336aebe-3a98-4bf5-d365-7ac488393876"
|
||
},
|
||
"execution_count": 10,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"There are 4610 rows and 2 columns\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# explore unique labels\n",
|
||
"print(IntentDataFrame.label.unique())"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "hI50lziHG2de",
|
||
"outputId": "a673d3d1-929d-4ee6-d4ac-ae10cf6cf7fc"
|
||
},
|
||
"execution_count": 11,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"['greeting#req_start' 'req_temperature' 'thank#req_instruction'\n",
|
||
" 'confirm#req_instruction' 'req_repeat' 'confirm' 'confirm#req_repeat'\n",
|
||
" 'negate#thank' 'negate' 'req_amount' 'req_instruction'\n",
|
||
" 'confirm#req_parallel_action' 'req_amount#req_ingredient' 'thank#confirm'\n",
|
||
" 'req_use_all' 'thank' 'other' 'confirm#req_is_recipe_finished' 'req_tool'\n",
|
||
" 'confirm#req_duration' 'confirm#thank' 'affirm#req_instruction'\n",
|
||
" 'req_repeat#confirm' 'confirm#req_temperature'\n",
|
||
" 'confirm#req_is_recipe_ongoing' 'req_ingredient' 'confirm#req_amount'\n",
|
||
" 'thank#confirm#req_instruction' 'thank#req_repeat' 'req_duration'\n",
|
||
" 'thank#req_duration' 'confirm#thank#req_instruction'\n",
|
||
" 'thank#confirm#req_is_recipe_finished' 'req_repeat#thank'\n",
|
||
" 'greeting#req_title' 'req_start' 'confirm#other' 'affirm'\n",
|
||
" 'confirm#req_start' 'confirm#req_duration#req_is_recipe_finished'\n",
|
||
" 'affirm#req_amount' 'req_ingredient_list' 'thank#goodbye'\n",
|
||
" 'req_parallel_action' 'confirm#goodbye' 'affirm#req_ingredient'\n",
|
||
" 'thank#req_ingredient' 'thank#confirm#req_ingredient'\n",
|
||
" 'req_ingredient_list_length' 'other#req_instruction' 'affirm#req_start'\n",
|
||
" 'thank#req_is_recipe_ongoing' 'req_is_recipe_ongoing' 'goodbye'\n",
|
||
" 'req_ingredient_list#confirm' 'affirm#thank#other'\n",
|
||
" 'req_repeat#req_amount' 'other#req_repeat' 'confirm#req_tool'\n",
|
||
" 'req_is_recipe_finished' 'thank#req_parallel_action'\n",
|
||
" 'affirm#req_ingredient_list' 'confirm#req_ingredient' 'affirm#confirm'\n",
|
||
" 'confirm#req_ingredient_list_ends' 'req_title' 'req_ingredient_list_ends'\n",
|
||
" 'req_substitute' 'negate#req_instruction' 'thank#req_is_recipe_finished'\n",
|
||
" 'thank#req_ingredient_list' 'affirm#thank' 'thank#req_tool'\n",
|
||
" 'affirm#req_ingredient_list_length' 'confirm#req_substitute'\n",
|
||
" 'affirm#other' 'confirm#req_instruction#req_duration'\n",
|
||
" 'req_ingredient_list#req_ingredient_list_length' 'confirm#affirm'\n",
|
||
" 'affirm#thank#req_ingredient' 'confirm#req_use_all'\n",
|
||
" 'req_amount#req_substitute' 'req_instruction#req_duration'\n",
|
||
" 'negate#confirm#req_instruction' 'thank#other' 'greeting'\n",
|
||
" 'other#req_temperature' 'req_ingredient_list_length#confirm'\n",
|
||
" 'thank#confirm#req_duration' 'greeting#req_ingredient_list'\n",
|
||
" 'thank#req_amount']\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# explore which labels are the most and least common\n",
|
||
"IntentDataFrame.label.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "psWrXfoiG7FT",
|
||
"outputId": "30f87dba-7389-48fe-b2a1-260f49c7dccd"
|
||
},
|
||
"execution_count": 12,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"confirm#req_instruction 1222\n",
|
||
"confirm 407\n",
|
||
"req_instruction 320\n",
|
||
"thank 225\n",
|
||
"greeting#req_title 216\n",
|
||
" ... \n",
|
||
"other#req_instruction 1\n",
|
||
"req_repeat#thank 1\n",
|
||
"confirm#req_start 1\n",
|
||
"confirm#goodbye 1\n",
|
||
"thank#req_amount 1\n",
|
||
"Name: label, Length: 91, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 12
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# drop rows with multiple labels\n",
|
||
"# df = df[df[\"label\"].str.contains(\"#\")==False]\n",
|
||
"# df.label.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"id": "DrWLIe2XG_Lc"
|
||
},
|
||
"execution_count": 13,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Preprocessing\n"
|
||
],
|
||
"metadata": {
|
||
"id": "NPRJeAp0HMeD"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!pip install datasets"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "hKxs9_1hJAkX",
|
||
"outputId": "04df77b3-70ed-4023-dcb9-b78025c2a960"
|
||
},
|
||
"execution_count": 14,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
|
||
"Collecting datasets\n",
|
||
" Downloading datasets-2.8.0-py3-none-any.whl (452 kB)\n",
|
||
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/452.9 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.7/452.9 KB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.9/452.9 KB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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||
"\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\n",
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||
"Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
|
||
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0)\n",
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||
"Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
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||
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
|
||
"Collecting xxhash\n",
|
||
" Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
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||
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/213.0 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (21.3)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
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||
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.25.1)\n",
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||
"Collecting responses<0.19\n",
|
||
" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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||
"Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
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"Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1)\n",
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||
"Collecting huggingface-hub<1.0.0,>=0.2.0\n",
|
||
" Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
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||
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/182.4 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting multiprocess\n",
|
||
" Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
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"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/132.0 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
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"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
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"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
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"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
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"Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
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"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
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"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (3.9.0)\n",
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||
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.4.0)\n",
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||
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->datasets) (3.0.9)\n",
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"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
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"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
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||
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
|
||
"Collecting urllib3<1.27,>=1.21.1\n",
|
||
" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
|
||
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/140.6 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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||
"\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
|
||
"Installing collected packages: xxhash, urllib3, multiprocess, responses, huggingface-hub, datasets\n",
|
||
" Attempting uninstall: urllib3\n",
|
||
" Found existing installation: urllib3 1.24.3\n",
|
||
" Uninstalling urllib3-1.24.3:\n",
|
||
" Successfully uninstalled urllib3-1.24.3\n",
|
||
"Successfully installed datasets-2.8.0 huggingface-hub-0.11.1 multiprocess-0.70.14 responses-0.18.0 urllib3-1.26.14 xxhash-3.2.0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!pip install transformers"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "78woLKRZJQuM",
|
||
"outputId": "237c4aeb-631e-4192-f9ab-95e07ff0ee79"
|
||
},
|
||
"execution_count": 15,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
|
||
"Collecting transformers\n",
|
||
" Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
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"\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
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"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
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" Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
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"Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
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"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
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"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
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"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
|
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.26.14)\n",
|
||
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n",
|
||
"Installing collected packages: tokenizers, transformers\n",
|
||
"Successfully installed tokenizers-0.13.2 transformers-4.25.1\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
|
||
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
|
||
"# For example, here's several helpful packages to load\n",
|
||
"\n",
|
||
"import numpy as np # linear algebra\n",
|
||
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
|
||
"import json\n",
|
||
"import datasets #Hugging Face library\n",
|
||
"from transformers import DataCollatorWithPadding\n",
|
||
"from transformers import AutoTokenizer\n",
|
||
"from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
|
||
"# Input data files are available in the read-only \"../input/\" directory\n",
|
||
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
|
||
"\n",
|
||
"import os\n",
|
||
"#for dirname, _, filenames in os.walk(ModelPath):\n",
|
||
"# for filename in filenames:\n",
|
||
"# print(os.path.join(dirname, filename))\n",
|
||
"\n",
|
||
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
|
||
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
|
||
],
|
||
"metadata": {
|
||
"id": "7XuHyqLlItUN"
|
||
},
|
||
"execution_count": 16,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# replace the labels strings by label numbers \n",
|
||
"unique_labels = IntentDataFrame.label.unique()\n",
|
||
"LabelToIndex = {}\n",
|
||
"\n",
|
||
"for i in range(len(unique_labels)):\n",
|
||
" LabelToIndex[unique_labels[i]] = i\n",
|
||
"\n",
|
||
"IntentDataFrame[\"label\"]=IntentDataFrame[\"label\"].map(LabelToIndex)"
|
||
],
|
||
"metadata": {
|
||
"id": "tBSmKYURJ24M"
|
||
},
|
||
"execution_count": 17,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_data = IntentDataFrame.sample(frac=0.8, random_state=25)\n",
|
||
"test_data = IntentDataFrame.drop(train_data.index)\n",
|
||
"\n",
|
||
"train_data = datasets.Dataset.from_pandas(train_data)\n",
|
||
"test_data = datasets.Dataset.from_pandas(test_data)\n",
|
||
"\n",
|
||
"print(f\"No. of training examples: {train_data.shape[0]}\")\n",
|
||
"print(f\"No. of testing examples: {test_data.shape[0]}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ui6NutubLmKy",
|
||
"outputId": "8eb6adec-63c0-4fdb-a670-8dc29c51569f"
|
||
},
|
||
"execution_count": 18,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"No. of training examples: 3688\n",
|
||
"No. of testing examples: 922\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Import AutoTokenizer with checkpoint\"distilbert-base-uncased\"\n",
|
||
"\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 145,
|
||
"referenced_widgets": [
|
||
"16362f0d98b64c5795565889f10e0b8c",
|
||
"4c3632e9677d47978b0e84cf3b59ade1",
|
||
"0c537c762db644259239221bb5ed552a",
|
||
"f4d578feff814384b5a3156825d9fef6",
|
||
"b82d1b601a434414a1845bbba6da2991",
|
||
"f40a17458d704cd2a8fbf68729a4198a",
|
||
"49606632a7054f55b2ffa7afbe90a00d",
|
||
"ce3f68b81ed34c5e8bfc41bd2a7ad263",
|
||
"388864342bf0439f8e16639bf53b7efe",
|
||
"c7773a01e08e49ddb45e596726132756",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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|
||
"63b832e3fd544cbbbabe6a6244ac2370",
|
||
"ff55840fe82b43bfa926e023f08b80ba",
|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
"3eff74ecc5084695920dce706c01511b",
|
||
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|
||
]
|
||
},
|
||
"id": "uNtrv_OjL_tl",
|
||
"outputId": "f196dbee-db87-42a6-930d-29fa5092e403"
|
||
},
|
||
"execution_count": 19,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
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"Downloading: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "16362f0d98b64c5795565889f10e0b8c"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
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"output_type": "display_data",
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],
|
||
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "c8b4feb673cd4f1bbbdbabf6d37b0686"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
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"data": {
|
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"text/plain": [
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|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "0fc939f7fe4a4d13a97b6193966bbcb6"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "ff55840fe82b43bfa926e023f08b80ba"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Tokenization work on train_dataset\n",
|
||
"def preprocess_function(examples):\n",
|
||
" return tokenizer(examples[\"sentence\"], truncation=True, padding=True)\n",
|
||
"tokenize_train=train_data.map(preprocess_function,batched=True)\n",
|
||
"tokenize_test=test_data.map(preprocess_function,batched=True)\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 81,
|
||
"referenced_widgets": [
|
||
"fc3173dcfe2848f2957e3677ffd07f84",
|
||
"6e7bbc64b2a1465586ce2a43ba4b22fa",
|
||
"134e5052c9da476fba4d6029da3d85db",
|
||
"952ed3c3a0094bb5abfea63b43dcaad8",
|
||
"6c1d6efa8be14080845e3321e0f0c188",
|
||
"bc8dc1bfa9124c1db3e738e0385cb8b5",
|
||
"e3eff38137554e318822ac8973f2f915",
|
||
"b8afd916d93142ddb949630bbd382800",
|
||
"97fdc3080b40458c9986ac61bb94a98e",
|
||
"d658a5fcbc60462ebfc383e7003b52ce",
|
||
"0acf000f9e384831aa4edca59abbf10e",
|
||
"58883bf03b554d37abd656a35fa86db3",
|
||
"5bc293602e704324a39df56dd3c3e96e",
|
||
"3d9cc746e07c4def8f74158ac94c6063",
|
||
"ba077267515f4182bfcbc587fa08aed2",
|
||
"2fbeedcd09264f8bb9d9868e79d35370",
|
||
"735d50fd6d7940bb8cd777b8b81f5b54",
|
||
"9d21df9b173247808c912105c15ba8ff",
|
||
"2de2842792574204814e9a7b297c68da",
|
||
"44670957fddd457a9ae0aa5995fada74",
|
||
"6958744e38ca4d16a2fa124311702539",
|
||
"a3c21cde1af94715a1bd4e0d210f2062"
|
||
]
|
||
},
|
||
"id": "H7FFIuxuMmSi",
|
||
"outputId": "29e5ea78-eb6b-4d13-a13f-736c5fe40126"
|
||
},
|
||
"execution_count": 20,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
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" 0%| | 0/4 [00:00<?, ?ba/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "fc3173dcfe2848f2957e3677ffd07f84"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
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|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "58883bf03b554d37abd656a35fa86db3"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# data_collator\n",
|
||
"\n",
|
||
"data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
|
||
],
|
||
"metadata": {
|
||
"id": "hE_OmfhVM_Hj"
|
||
},
|
||
"execution_count": 21,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Build model \n",
|
||
"\n",
|
||
"model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=len(unique_labels))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 156,
|
||
"referenced_widgets": [
|
||
"b4a54a4d93634b91aefa01776c400173",
|
||
"637a668c4ff845648d08729e20f8d7f0",
|
||
"d0704b2725be49f3a079734173086f85",
|
||
"86549aa685e84d39a906ed93742d66f9",
|
||
"6c0d510901ec428d8440030c6434839d",
|
||
"172aa9e4059448e7b6a64e034e1cf171",
|
||
"05c7bab10ade4f048b6ff37e17e61238",
|
||
"1de2aaab2df74709a7ddd83deab322a8",
|
||
"667bcef6967c4a01850e1c7f3d402848",
|
||
"d5b5277e37d4467290abb401fc47ae80",
|
||
"0922ba7734df437c9a3417d45c5274da"
|
||
]
|
||
},
|
||
"id": "q9D9XGh3NqOX",
|
||
"outputId": "4112990e-769e-43fe-d5bd-ce34fc1be630"
|
||
},
|
||
"execution_count": 22,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading: 0%| | 0.00/268M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "b4a54a4d93634b91aefa01776c400173"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.weight']\n",
|
||
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
||
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
||
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'classifier.bias']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from datasets import load_metric\n",
|
||
"metric = load_metric('accuracy')\n",
|
||
"\n",
|
||
"def compute_metrics(eval_pred):\n",
|
||
" predictions, labels = eval_pred\n",
|
||
" predictions = np.argmax(predictions, axis=1)\n",
|
||
" return metric.compute(predictions=predictions, references=labels)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 104,
|
||
"referenced_widgets": [
|
||
"c42dd35421e842699a76a38d9b4c6124",
|
||
"7ae1af56270b476e9c84ec9381dbfe7f",
|
||
"9680cdca7afe4f81a8aa6c1f6ed48f18",
|
||
"dbc2571f8320465f8c9860963d8b2290",
|
||
"41ae6d24c1f64db890c959634b613f29",
|
||
"f02ca95e42654721aedc22d5d617e601",
|
||
"c1423b8980b64f45b9a815a866ba260c",
|
||
"9117cc650dc04ed68ce67872291f2f6c",
|
||
"00cf005090ec4a0abc33e683fabb65a0",
|
||
"fa4c644c022a4a99bb9b709bada15f84",
|
||
"99588acc00a54035a17790b2e7d18651"
|
||
]
|
||
},
|
||
"id": "QdY5KKkIR7w2",
|
||
"outputId": "737515f7-3828-479d-cdd9-adcb29c6d243"
|
||
},
|
||
"execution_count": 30,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"<ipython-input-30-7d137328fd2b>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
|
||
" metric = load_metric('accuracy')\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading builder script: 0%| | 0.00/1.65k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "c42dd35421e842699a76a38d9b4c6124"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Model fine tuning training\n",
|
||
"training_args = TrainingArguments(\n",
|
||
" output_dir=\"/content/results\",\n",
|
||
" learning_rate=2e-5,\n",
|
||
" per_device_train_batch_size=8,\n",
|
||
" per_device_eval_batch_size=8,\n",
|
||
" num_train_epochs=20,\n",
|
||
" weight_decay=0.01,\n",
|
||
" evaluation_strategy=\"epoch\"\n",
|
||
")\n",
|
||
"\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset=tokenize_train,\n",
|
||
" eval_dataset=tokenize_test,\n",
|
||
" compute_metrics=compute_metrics,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" data_collator=data_collator,\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"trainer.train()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "1pi7xo9bN7Zn",
|
||
"outputId": "87afae93-1334-48f1-d376-741f9eea4f9c"
|
||
},
|
||
"execution_count": 33,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"PyTorch: setting up devices\n",
|
||
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
|
||
"The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
|
||
" warnings.warn(\n",
|
||
"***** Running training *****\n",
|
||
" Num examples = 3688\n",
|
||
" Num Epochs = 20\n",
|
||
" Instantaneous batch size per device = 8\n",
|
||
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
|
||
" Gradient Accumulation steps = 1\n",
|
||
" Total optimization steps = 9220\n",
|
||
" Number of trainable parameters = 67023451\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div>\n",
|
||
" \n",
|
||
" <progress value='9220' max='9220' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [9220/9220 10:23, Epoch 20/20]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Epoch</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" <th>Validation Loss</th>\n",
|
||
" <th>Accuracy</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>No log</td>\n",
|
||
" <td>1.174354</td>\n",
|
||
" <td>0.822126</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.066100</td>\n",
|
||
" <td>1.392280</td>\n",
|
||
" <td>0.784165</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.061900</td>\n",
|
||
" <td>1.246237</td>\n",
|
||
" <td>0.823210</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.056900</td>\n",
|
||
" <td>1.324116</td>\n",
|
||
" <td>0.813449</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>5</td>\n",
|
||
" <td>0.089800</td>\n",
|
||
" <td>1.304054</td>\n",
|
||
" <td>0.819957</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>6</td>\n",
|
||
" <td>0.103200</td>\n",
|
||
" <td>1.212749</td>\n",
|
||
" <td>0.818872</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>7</td>\n",
|
||
" <td>0.093600</td>\n",
|
||
" <td>1.229501</td>\n",
|
||
" <td>0.819957</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>8</td>\n",
|
||
" <td>0.098000</td>\n",
|
||
" <td>1.279489</td>\n",
|
||
" <td>0.817787</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>9</td>\n",
|
||
" <td>0.083700</td>\n",
|
||
" <td>1.188151</td>\n",
|
||
" <td>0.821041</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>10</td>\n",
|
||
" <td>0.068900</td>\n",
|
||
" <td>1.274577</td>\n",
|
||
" <td>0.816703</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>11</td>\n",
|
||
" <td>0.063100</td>\n",
|
||
" <td>1.275792</td>\n",
|
||
" <td>0.824295</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>12</td>\n",
|
||
" <td>0.063700</td>\n",
|
||
" <td>1.263834</td>\n",
|
||
" <td>0.825380</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>13</td>\n",
|
||
" <td>0.063700</td>\n",
|
||
" <td>1.323240</td>\n",
|
||
" <td>0.812364</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>14</td>\n",
|
||
" <td>0.055600</td>\n",
|
||
" <td>1.266973</td>\n",
|
||
" <td>0.825380</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>15</td>\n",
|
||
" <td>0.049200</td>\n",
|
||
" <td>1.295590</td>\n",
|
||
" <td>0.824295</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>16</td>\n",
|
||
" <td>0.049600</td>\n",
|
||
" <td>1.288514</td>\n",
|
||
" <td>0.829718</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0.044600</td>\n",
|
||
" <td>1.282528</td>\n",
|
||
" <td>0.824295</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>18</td>\n",
|
||
" <td>0.041200</td>\n",
|
||
" <td>1.285815</td>\n",
|
||
" <td>0.823210</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>19</td>\n",
|
||
" <td>0.050200</td>\n",
|
||
" <td>1.290950</td>\n",
|
||
" <td>0.821041</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>20</td>\n",
|
||
" <td>0.042100</td>\n",
|
||
" <td>1.288992</td>\n",
|
||
" <td>0.819957</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
]
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-500\n",
|
||
"Configuration saved in /content/results/checkpoint-500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-1000\n",
|
||
"Configuration saved in /content/results/checkpoint-1000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-1000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-1000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-1000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-1500\n",
|
||
"Configuration saved in /content/results/checkpoint-1500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-1500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-1500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-1500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-2000\n",
|
||
"Configuration saved in /content/results/checkpoint-2000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-2000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-2000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-2000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-2500\n",
|
||
"Configuration saved in /content/results/checkpoint-2500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-2500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-2500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-2500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-3000\n",
|
||
"Configuration saved in /content/results/checkpoint-3000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-3000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-3000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-3000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-3500\n",
|
||
"Configuration saved in /content/results/checkpoint-3500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-3500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-3500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-3500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-4000\n",
|
||
"Configuration saved in /content/results/checkpoint-4000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-4000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-4000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-4000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-4500\n",
|
||
"Configuration saved in /content/results/checkpoint-4500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-4500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-4500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-4500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-5000\n",
|
||
"Configuration saved in /content/results/checkpoint-5000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-5000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-5000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-5000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-5500\n",
|
||
"Configuration saved in /content/results/checkpoint-5500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-5500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-5500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-5500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-6000\n",
|
||
"Configuration saved in /content/results/checkpoint-6000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-6000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-6000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-6000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-6500\n",
|
||
"Configuration saved in /content/results/checkpoint-6500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-6500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-6500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-6500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-7000\n",
|
||
"Configuration saved in /content/results/checkpoint-7000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-7000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-7000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-7000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-7500\n",
|
||
"Configuration saved in /content/results/checkpoint-7500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-7500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-7500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-7500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-8000\n",
|
||
"Configuration saved in /content/results/checkpoint-8000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-8000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-8000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-8000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-8500\n",
|
||
"Configuration saved in /content/results/checkpoint-8500/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-8500/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-8500/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-8500/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"Saving model checkpoint to /content/results/checkpoint-9000\n",
|
||
"Configuration saved in /content/results/checkpoint-9000/config.json\n",
|
||
"Model weights saved in /content/results/checkpoint-9000/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/results/checkpoint-9000/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/results/checkpoint-9000/special_tokens_map.json\n",
|
||
"The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: __index_level_0__, sentence. If __index_level_0__, sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Evaluation *****\n",
|
||
" Num examples = 922\n",
|
||
" Batch size = 8\n",
|
||
"\n",
|
||
"\n",
|
||
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"TrainOutput(global_step=9220, training_loss=0.06487055326490754, metrics={'train_runtime': 624.1342, 'train_samples_per_second': 118.18, 'train_steps_per_second': 14.772, 'total_flos': 869726868928704.0, 'train_loss': 0.06487055326490754, 'epoch': 20.0})"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 33
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def SentenceClassifier(InputSentence):\n",
|
||
" \"\"\" Take a sentence as input, return the corresponding label\n",
|
||
" \n",
|
||
" dependencies : tokenizer, trainer\n",
|
||
" \"\"\"\n",
|
||
" \n",
|
||
" def preprocess_function(examples):\n",
|
||
" return tokenizer(examples[\"sentence\"], truncation=True, padding=True)\n",
|
||
" \n",
|
||
" # here, we are keeping the input as a Dataset, which could allow us to reuse the code\n",
|
||
" # to answer many questions at once\n",
|
||
" InputSentenceDFData = {'sentence' : [InputSentence]}\n",
|
||
" InputSentenceDataFrame = pd.DataFrame(data = InputSentenceDFData)\n",
|
||
" InputSentenceDataset = datasets.Dataset.from_pandas(InputSentenceDataFrame)\n",
|
||
" Tokenised_InputSentence = InputSentenceDataset.map(preprocess_function,batched=False)\n",
|
||
" \n",
|
||
" LabelScores = trainer.predict(Tokenised_InputSentence)\n",
|
||
" BestLabel = LabelScores.predictions.argmax(1)\n",
|
||
" \n",
|
||
" OutputLabelName = list(LabelToIndex.keys())[list(LabelToIndex.values()).index(BestLabel[0])]\n",
|
||
" \n",
|
||
" return OutputLabelName"
|
||
],
|
||
"metadata": {
|
||
"id": "VayZySKQXGA3"
|
||
},
|
||
"execution_count": 36,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"InputSentence = \"yes please\".lower()\n",
|
||
"OutputLabel = SentenceClassifier(InputSentence)\n",
|
||
"print(f'Your question was : \"{InputSentence}\" it was classified as : \"{OutputLabel}\"')"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 156,
|
||
"referenced_widgets": [
|
||
"5234189fff004658bc07ba90639eb762",
|
||
"ce744f6d339e4ffdaf2a5c7d8445a9de",
|
||
"3c0679a3691046eda0a0508c55116a7d",
|
||
"198aabca329a43b49bb11937be109137",
|
||
"65bdd85904ab44ab835eb8d7e28db4ac",
|
||
"1f785cff415b4f81af18b87622eb620a",
|
||
"fc4cb3050dc34f7bab7339f3043456c1",
|
||
"628d5b7bfb2449788e54c68093fca6ed",
|
||
"165f3401fae843ab9158e6ebbbf58c84",
|
||
"187067e48e4b41b0844d4e77ff9a5014",
|
||
"27daf9a6b18b4e2d86c40ed900fcef09"
|
||
]
|
||
},
|
||
"id": "QUrDRrYoXNZI",
|
||
"outputId": "1d0cdec9-63b3-4502-fff2-dd32f00e79b8"
|
||
},
|
||
"execution_count": 62,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0/1 [00:00<?, ?ex/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "5234189fff004658bc07ba90639eb762"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: sentence. If sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Prediction *****\n",
|
||
" Num examples = 1\n",
|
||
" Batch size = 8\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
],
|
||
"text/html": []
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Your question was : \"yes please\" it was classified as : \"affirm\"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Save the model and tokenizer locally\n",
|
||
"!mkdir /content/CookDial/working\n",
|
||
"!mkdir /content/CookDial/working/model/\n",
|
||
"!mkdir /content/CookDial/working/tokenizer/\n",
|
||
"\n",
|
||
"ModelPath = \"/content/CookDial/working/model/\"\n",
|
||
"TokenizerPath = \"/content/CookDial/working/tokenizer/\"\n",
|
||
"\n",
|
||
"if os.path.isdir(ModelPath):\n",
|
||
" model.save_pretrained(ModelPath)\n",
|
||
" print(\"model ok\")\n",
|
||
"if os.path.isdir(TokenizerPath):\n",
|
||
" tokenizer.save_pretrained(TokenizerPath)\n",
|
||
" print(\"tokenizer ok\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "7B_sHJY8XQ5r",
|
||
"outputId": "bf75ca90-ea8c-4b05-c0bc-f9e14cbaeea7"
|
||
},
|
||
"execution_count": 65,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Configuration saved in /content/CookDial/working/model/config.json\n",
|
||
"Model weights saved in /content/CookDial/working/model/pytorch_model.bin\n",
|
||
"tokenizer config file saved in /content/CookDial/working/tokenizer/tokenizer_config.json\n",
|
||
"Special tokens file saved in /content/CookDial/working/tokenizer/special_tokens_map.json\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"model ok\n",
|
||
"tokenizer ok\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Load the model and tokenizer from a local path\\\n",
|
||
"LocalModel = AutoModelForSequenceClassification.from_pretrained(ModelPath,num_labels=len(unique_labels))\n",
|
||
"LocalTokenizer = AutoTokenizer.from_pretrained(TokenizerPath)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "g63HNXuHZhCC",
|
||
"outputId": "519f7abc-7bf0-44bb-9b89-d19f252c5a43"
|
||
},
|
||
"execution_count": 66,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"loading configuration file /content/CookDial/working/model/config.json\n",
|
||
"Model config DistilBertConfig {\n",
|
||
" \"_name_or_path\": \"/content/CookDial/working/model/\",\n",
|
||
" \"activation\": \"gelu\",\n",
|
||
" \"architectures\": [\n",
|
||
" \"DistilBertForSequenceClassification\"\n",
|
||
" ],\n",
|
||
" \"attention_dropout\": 0.1,\n",
|
||
" \"dim\": 768,\n",
|
||
" \"dropout\": 0.1,\n",
|
||
" \"hidden_dim\": 3072,\n",
|
||
" \"id2label\": {\n",
|
||
" \"0\": \"LABEL_0\",\n",
|
||
" \"1\": \"LABEL_1\",\n",
|
||
" \"2\": \"LABEL_2\",\n",
|
||
" \"3\": \"LABEL_3\",\n",
|
||
" \"4\": \"LABEL_4\",\n",
|
||
" \"5\": \"LABEL_5\",\n",
|
||
" \"6\": \"LABEL_6\",\n",
|
||
" \"7\": \"LABEL_7\",\n",
|
||
" \"8\": \"LABEL_8\",\n",
|
||
" \"9\": \"LABEL_9\",\n",
|
||
" \"10\": \"LABEL_10\",\n",
|
||
" \"11\": \"LABEL_11\",\n",
|
||
" \"12\": \"LABEL_12\",\n",
|
||
" \"13\": \"LABEL_13\",\n",
|
||
" \"14\": \"LABEL_14\",\n",
|
||
" \"15\": \"LABEL_15\",\n",
|
||
" \"16\": \"LABEL_16\",\n",
|
||
" \"17\": \"LABEL_17\",\n",
|
||
" \"18\": \"LABEL_18\",\n",
|
||
" \"19\": \"LABEL_19\",\n",
|
||
" \"20\": \"LABEL_20\",\n",
|
||
" \"21\": \"LABEL_21\",\n",
|
||
" \"22\": \"LABEL_22\",\n",
|
||
" \"23\": \"LABEL_23\",\n",
|
||
" \"24\": \"LABEL_24\",\n",
|
||
" \"25\": \"LABEL_25\",\n",
|
||
" \"26\": \"LABEL_26\",\n",
|
||
" \"27\": \"LABEL_27\",\n",
|
||
" \"28\": \"LABEL_28\",\n",
|
||
" \"29\": \"LABEL_29\",\n",
|
||
" \"30\": \"LABEL_30\",\n",
|
||
" \"31\": \"LABEL_31\",\n",
|
||
" \"32\": \"LABEL_32\",\n",
|
||
" \"33\": \"LABEL_33\",\n",
|
||
" \"34\": \"LABEL_34\",\n",
|
||
" \"35\": \"LABEL_35\",\n",
|
||
" \"36\": \"LABEL_36\",\n",
|
||
" \"37\": \"LABEL_37\",\n",
|
||
" \"38\": \"LABEL_38\",\n",
|
||
" \"39\": \"LABEL_39\",\n",
|
||
" \"40\": \"LABEL_40\",\n",
|
||
" \"41\": \"LABEL_41\",\n",
|
||
" \"42\": \"LABEL_42\",\n",
|
||
" \"43\": \"LABEL_43\",\n",
|
||
" \"44\": \"LABEL_44\",\n",
|
||
" \"45\": \"LABEL_45\",\n",
|
||
" \"46\": \"LABEL_46\",\n",
|
||
" \"47\": \"LABEL_47\",\n",
|
||
" \"48\": \"LABEL_48\",\n",
|
||
" \"49\": \"LABEL_49\",\n",
|
||
" \"50\": \"LABEL_50\",\n",
|
||
" \"51\": \"LABEL_51\",\n",
|
||
" \"52\": \"LABEL_52\",\n",
|
||
" \"53\": \"LABEL_53\",\n",
|
||
" \"54\": \"LABEL_54\",\n",
|
||
" \"55\": \"LABEL_55\",\n",
|
||
" \"56\": \"LABEL_56\",\n",
|
||
" \"57\": \"LABEL_57\",\n",
|
||
" \"58\": \"LABEL_58\",\n",
|
||
" \"59\": \"LABEL_59\",\n",
|
||
" \"60\": \"LABEL_60\",\n",
|
||
" \"61\": \"LABEL_61\",\n",
|
||
" \"62\": \"LABEL_62\",\n",
|
||
" \"63\": \"LABEL_63\",\n",
|
||
" \"64\": \"LABEL_64\",\n",
|
||
" \"65\": \"LABEL_65\",\n",
|
||
" \"66\": \"LABEL_66\",\n",
|
||
" \"67\": \"LABEL_67\",\n",
|
||
" \"68\": \"LABEL_68\",\n",
|
||
" \"69\": \"LABEL_69\",\n",
|
||
" \"70\": \"LABEL_70\",\n",
|
||
" \"71\": \"LABEL_71\",\n",
|
||
" \"72\": \"LABEL_72\",\n",
|
||
" \"73\": \"LABEL_73\",\n",
|
||
" \"74\": \"LABEL_74\",\n",
|
||
" \"75\": \"LABEL_75\",\n",
|
||
" \"76\": \"LABEL_76\",\n",
|
||
" \"77\": \"LABEL_77\",\n",
|
||
" \"78\": \"LABEL_78\",\n",
|
||
" \"79\": \"LABEL_79\",\n",
|
||
" \"80\": \"LABEL_80\",\n",
|
||
" \"81\": \"LABEL_81\",\n",
|
||
" \"82\": \"LABEL_82\",\n",
|
||
" \"83\": \"LABEL_83\",\n",
|
||
" \"84\": \"LABEL_84\",\n",
|
||
" \"85\": \"LABEL_85\",\n",
|
||
" \"86\": \"LABEL_86\",\n",
|
||
" \"87\": \"LABEL_87\",\n",
|
||
" \"88\": \"LABEL_88\",\n",
|
||
" \"89\": \"LABEL_89\",\n",
|
||
" \"90\": \"LABEL_90\"\n",
|
||
" },\n",
|
||
" \"initializer_range\": 0.02,\n",
|
||
" \"label2id\": {\n",
|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" \"LABEL_50\": 50,\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" \"LABEL_69\": 69,\n",
|
||
" \"LABEL_7\": 7,\n",
|
||
" \"LABEL_70\": 70,\n",
|
||
" \"LABEL_71\": 71,\n",
|
||
" \"LABEL_72\": 72,\n",
|
||
" \"LABEL_73\": 73,\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" \"LABEL_79\": 79,\n",
|
||
" \"LABEL_8\": 8,\n",
|
||
" \"LABEL_80\": 80,\n",
|
||
" \"LABEL_81\": 81,\n",
|
||
" \"LABEL_82\": 82,\n",
|
||
" \"LABEL_83\": 83,\n",
|
||
" \"LABEL_84\": 84,\n",
|
||
" \"LABEL_85\": 85,\n",
|
||
" \"LABEL_86\": 86,\n",
|
||
" \"LABEL_87\": 87,\n",
|
||
" \"LABEL_88\": 88,\n",
|
||
" \"LABEL_89\": 89,\n",
|
||
" \"LABEL_9\": 9,\n",
|
||
" \"LABEL_90\": 90\n",
|
||
" },\n",
|
||
" \"max_position_embeddings\": 512,\n",
|
||
" \"model_type\": \"distilbert\",\n",
|
||
" \"n_heads\": 12,\n",
|
||
" \"n_layers\": 6,\n",
|
||
" \"pad_token_id\": 0,\n",
|
||
" \"problem_type\": \"single_label_classification\",\n",
|
||
" \"qa_dropout\": 0.1,\n",
|
||
" \"seq_classif_dropout\": 0.2,\n",
|
||
" \"sinusoidal_pos_embds\": false,\n",
|
||
" \"tie_weights_\": true,\n",
|
||
" \"torch_dtype\": \"float32\",\n",
|
||
" \"transformers_version\": \"4.25.1\",\n",
|
||
" \"vocab_size\": 30522\n",
|
||
"}\n",
|
||
"\n",
|
||
"loading weights file /content/CookDial/working/model/pytorch_model.bin\n",
|
||
"All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n",
|
||
"\n",
|
||
"All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at /content/CookDial/working/model/.\n",
|
||
"If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n",
|
||
"loading file vocab.txt\n",
|
||
"loading file tokenizer.json\n",
|
||
"loading file added_tokens.json\n",
|
||
"loading file special_tokens_map.json\n",
|
||
"loading file tokenizer_config.json\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def LocalSentenceClassifier(InputSentence):\n",
|
||
" \"\"\" Take a sentence as input, return the corresponding label\n",
|
||
" \n",
|
||
" dependencies : LocalTokenizer, LocalModel\n",
|
||
" We use tokenizer2 and trainer2 instead of tokeninzer and trainer\n",
|
||
" to be sure that this function works with the data saved and load locally\n",
|
||
" \"\"\"\n",
|
||
" \n",
|
||
" trainer = Trainer(\n",
|
||
" model=LocalModel,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset=tokenize_train,\n",
|
||
" #eval_dataset=tokenize_test, Here, we work with the entire dataset as training data\n",
|
||
" #compute_metrics=compute_metrics,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" data_collator=data_collator,\n",
|
||
" )\n",
|
||
" \n",
|
||
" def preprocess_function(examples):\n",
|
||
" return LocalTokenizer(examples[\"sentence\"], truncation=True, padding=True)\n",
|
||
" \n",
|
||
" # here, we are keeping the input as a Dataset, which could allow us to reuse the code\n",
|
||
" # to answer many questions at once\n",
|
||
" InputSentenceDFData = {'sentence' : [InputSentence]}\n",
|
||
" InputSentenceDataFrame = pd.DataFrame(data = InputSentenceDFData)\n",
|
||
" InputSentenceDataset = datasets.Dataset.from_pandas(InputSentenceDataFrame)\n",
|
||
" Tokenised_InputSentence = InputSentenceDataset.map(preprocess_function,batched=False)\n",
|
||
" \n",
|
||
" LabelScores = trainer.predict(Tokenised_InputSentence)\n",
|
||
" BestLabel = LabelScores.predictions.argmax(1)\n",
|
||
" \n",
|
||
" OutputLabelName = list(LabelToIndex.keys())[list(LabelToIndex.values()).index(BestLabel[0])]\n",
|
||
" \n",
|
||
" return OutputLabelName"
|
||
],
|
||
"metadata": {
|
||
"id": "9Vuln6gdaVGY"
|
||
},
|
||
"execution_count": 69,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"InputSentence = \"ok next step\"\n",
|
||
"OutputLabel = LocalSentenceClassifier(InputSentence)\n",
|
||
"print(f'Your question was : \"{InputSentence}\" it was classified as : \"{OutputLabel}\"')"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 156,
|
||
"referenced_widgets": [
|
||
"e9113237710c4692a4a7ab64a6793220",
|
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"ae1ba4d0e7304a93b60e9249bc65e009",
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"37f3ac59e9414797a531cbd7ad8a4ee6",
|
||
"8cbdc0c1ff074a21b12b65437c6f81e5",
|
||
"c1847c02356f4341a91c061ab96313e9",
|
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"5b64f35757df4f70acc6d1a42e3e9083",
|
||
"201eea97897649a3ab7ad114ff8c7b6f",
|
||
"ffdf6b640a1840e491a5ef281d10774a",
|
||
"ee3c98646dd6402680287e3fc1086a2b",
|
||
"8812dac4c9ce49e8a8e0b90372016262",
|
||
"9063989066d24362b0f49189c32b70b9"
|
||
]
|
||
},
|
||
"id": "WR2fmxg_aXS8",
|
||
"outputId": "fa18f374-90e4-4e83-c632-a4d7b139f928"
|
||
},
|
||
"execution_count": 70,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0/1 [00:00<?, ?ex/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "e9113237710c4692a4a7ab64a6793220"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: sentence. If sentence are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"***** Running Prediction *****\n",
|
||
" Num examples = 1\n",
|
||
" Batch size = 8\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
],
|
||
"text/html": []
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Your question was : \"ok next step\" it was classified as : \"req_instruction\"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# used train script: https://www.kaggle.com/code/philanoe/intent-classifier-training"
|
||
],
|
||
"metadata": {
|
||
"id": "_vGry77OdDyg"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |