7066 lines
259 KiB
Plaintext
7066 lines
259 KiB
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "AJJ9B2v6-Xna",
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"outputId": "49913840-3c1e-4647-95c3-b75192aacd25"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting datasets\n",
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" Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting transformers\n",
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" Downloading transformers-4.26.0-py3-none-any.whl (6.3 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m69.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
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"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.0.2)\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: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\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: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.25.1)\n",
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"Collecting multiprocess\n",
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" Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting responses<0.19\n",
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" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\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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"Collecting xxhash\n",
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" Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting huggingface-hub<1.0.0,>=0.2.0\n",
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" Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
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"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: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (23.0)\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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"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: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\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[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n",
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"Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.7.3)\n",
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"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
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"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\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: 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: 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: 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: 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: 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: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0)\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",
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"Collecting urllib3<1.27,>=1.21.1\n",
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" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
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"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",
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"Installing collected packages: tokenizers, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
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" Attempting uninstall: urllib3\n",
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" Found existing installation: urllib3 1.24.3\n",
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" Uninstalling urllib3-1.24.3:\n",
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" Successfully uninstalled urllib3-1.24.3\n",
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"Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.0 urllib3-1.26.14 xxhash-3.2.0\n"
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]
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}
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],
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"source": [
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"! pip install datasets transformers torch scikit-learn"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"device"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 35
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},
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"id": "-hCpa7A9crhz",
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"outputId": "6738a397-d1d0-4d65-fd71-f69dc7b16498"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'cuda'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 5
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000,
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"referenced_widgets": [
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"text": [
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"Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
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"- This IS expected if you are initializing RobertaForSequenceClassification 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",
|
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"- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
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"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']\n",
|
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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"Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"
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"text": [
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"Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
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"name": "stderr",
|
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"text": [
|
|||
|
|
"Using cuda_amp half precision backend\n",
|
|||
|
|
"The following columns in the training set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.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 = 16000\n",
|
|||
|
|
" Num Epochs = 5\n",
|
|||
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|
" Instantaneous batch size per device = 32\n",
|
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" Total train batch size (w. parallel, distributed & accumulation) = 512\n",
|
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|
|
" Gradient Accumulation steps = 16\n",
|
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|
|
" Total optimization steps = 155\n",
|
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|
" Number of trainable parameters = 124650246\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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" [155/155 06:28, Epoch 4/5]\n",
|
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|
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|
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|
|
" <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",
|
|||
|
|
" <th>F1</th>\n",
|
|||
|
|
" <th>Precision</th>\n",
|
|||
|
|
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|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1.089600</td>\n",
|
|||
|
|
" <td>0.782759</td>\n",
|
|||
|
|
" <td>0.711500</td>\n",
|
|||
|
|
" <td>0.711500</td>\n",
|
|||
|
|
" <td>0.711500</td>\n",
|
|||
|
|
" <td>0.711500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.380000</td>\n",
|
|||
|
|
" <td>0.267057</td>\n",
|
|||
|
|
" <td>0.900000</td>\n",
|
|||
|
|
" <td>0.900000</td>\n",
|
|||
|
|
" <td>0.900000</td>\n",
|
|||
|
|
" <td>0.900000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>0.186000</td>\n",
|
|||
|
|
" <td>0.183795</td>\n",
|
|||
|
|
" <td>0.923000</td>\n",
|
|||
|
|
" <td>0.923000</td>\n",
|
|||
|
|
" <td>0.923000</td>\n",
|
|||
|
|
" <td>0.923000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>0.156900</td>\n",
|
|||
|
|
" <td>0.163963</td>\n",
|
|||
|
|
" <td>0.934500</td>\n",
|
|||
|
|
" <td>0.934500</td>\n",
|
|||
|
|
" <td>0.934500</td>\n",
|
|||
|
|
" <td>0.934500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>0.130100</td>\n",
|
|||
|
|
" <td>0.160831</td>\n",
|
|||
|
|
" <td>0.933500</td>\n",
|
|||
|
|
" <td>0.933500</td>\n",
|
|||
|
|
" <td>0.933500</td>\n",
|
|||
|
|
" <td>0.933500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table><p>"
|
|||
|
|
]
|
|||
|
|
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|
|||
|
|
"metadata": {}
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
|
|||
|
|
"***** Running Evaluation *****\n",
|
|||
|
|
" Num examples = 2000\n",
|
|||
|
|
" Batch size = 32\n",
|
|||
|
|
"Saving model checkpoint to ./output/checkpoint-31\n",
|
|||
|
|
"Configuration saved in ./output/checkpoint-31/config.json\n",
|
|||
|
|
"Model weights saved in ./output/checkpoint-31/pytorch_model.bin\n",
|
|||
|
|
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
|
|||
|
|
"***** Running Evaluation *****\n",
|
|||
|
|
" Num examples = 2000\n",
|
|||
|
|
" Batch size = 32\n",
|
|||
|
|
"Saving model checkpoint to ./output/checkpoint-62\n",
|
|||
|
|
"Configuration saved in ./output/checkpoint-62/config.json\n",
|
|||
|
|
"Model weights saved in ./output/checkpoint-62/pytorch_model.bin\n",
|
|||
|
|
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
|
|||
|
|
"***** Running Evaluation *****\n",
|
|||
|
|
" Num examples = 2000\n",
|
|||
|
|
" Batch size = 32\n",
|
|||
|
|
"Saving model checkpoint to ./output/checkpoint-93\n",
|
|||
|
|
"Configuration saved in ./output/checkpoint-93/config.json\n",
|
|||
|
|
"Model weights saved in ./output/checkpoint-93/pytorch_model.bin\n",
|
|||
|
|
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
|
|||
|
|
"***** Running Evaluation *****\n",
|
|||
|
|
" Num examples = 2000\n",
|
|||
|
|
" Batch size = 32\n",
|
|||
|
|
"Saving model checkpoint to ./output/checkpoint-124\n",
|
|||
|
|
"Configuration saved in ./output/checkpoint-124/config.json\n",
|
|||
|
|
"Model weights saved in ./output/checkpoint-124/pytorch_model.bin\n",
|
|||
|
|
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
|
|||
|
|
"***** Running Evaluation *****\n",
|
|||
|
|
" Num examples = 2000\n",
|
|||
|
|
" Batch size = 32\n",
|
|||
|
|
"Saving model checkpoint to ./output/checkpoint-155\n",
|
|||
|
|
"Configuration saved in ./output/checkpoint-155/config.json\n",
|
|||
|
|
"Model weights saved in ./output/checkpoint-155/pytorch_model.bin\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"Loading best model from ./output/checkpoint-155 (score: 0.1608307659626007).\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"TrainOutput(global_step=155, training_loss=0.495477742725803, metrics={'train_runtime': 393.8758, 'train_samples_per_second': 203.11, 'train_steps_per_second': 0.394, 'total_flos': 3612118290333696.0, 'train_loss': 0.495477742725803, 'epoch': 4.99})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 2
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from datasets import load_dataset\n",
|
|||
|
|
"from transformers import RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, Trainer, TrainingArguments\n",
|
|||
|
|
"import torch\n",
|
|||
|
|
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Initializing a RoBERTa configuration\n",
|
|||
|
|
"configuration = RobertaConfig(vocab_size=2048)\n",
|
|||
|
|
"model = RobertaForSequenceClassification(configuration).from_pretrained(\"roberta-base\", num_labels=6)\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.cuda()\n",
|
|||
|
|
"tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def tokenization(batched_text):\n",
|
|||
|
|
" return tokenizer(batched_text['text'], padding = True, truncation=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def compute_metrics(pred):\n",
|
|||
|
|
" labels = pred.label_ids\n",
|
|||
|
|
" preds = pred.predictions.argmax(-1)\n",
|
|||
|
|
" precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')\n",
|
|||
|
|
" acc = accuracy_score(labels, preds)\n",
|
|||
|
|
" return {\n",
|
|||
|
|
" 'accuracy': acc,\n",
|
|||
|
|
" 'f1': f1,\n",
|
|||
|
|
" 'precision': precision,\n",
|
|||
|
|
" 'recall': recall\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Accessing the model configuration\n",
|
|||
|
|
"configuration = model.config\n",
|
|||
|
|
"dataset = load_dataset(\"emotion\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_data= dataset[\"train\"]\n",
|
|||
|
|
"test_data = dataset[\"test\"]\n",
|
|||
|
|
"eval_data = dataset[\"validation\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_data = train_data.map(tokenization, batched=True, batch_size=len(train_data))\n",
|
|||
|
|
"eval_data = eval_data.map(tokenization, batched=True, batch_size=len(eval_data))\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
|
|||
|
|
"eval_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"training_args = TrainingArguments(\n",
|
|||
|
|
" output_dir=\"./output\",\n",
|
|||
|
|
" num_train_epochs=5,\n",
|
|||
|
|
" per_device_train_batch_size = 32,\n",
|
|||
|
|
" gradient_accumulation_steps = 16, \n",
|
|||
|
|
" per_device_eval_batch_size= 32,\n",
|
|||
|
|
" evaluation_strategy = \"epoch\",\n",
|
|||
|
|
" save_strategy = \"epoch\",\n",
|
|||
|
|
" disable_tqdm = False, \n",
|
|||
|
|
" load_best_model_at_end=True,\n",
|
|||
|
|
" warmup_steps=10,\n",
|
|||
|
|
" weight_decay=0.01,\n",
|
|||
|
|
" logging_steps = 4,\n",
|
|||
|
|
" fp16 = True,\n",
|
|||
|
|
" dataloader_num_workers = 2,\n",
|
|||
|
|
" run_name = 'roberta-classification'\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"trainer = Trainer(\n",
|
|||
|
|
" model=model,\n",
|
|||
|
|
" args=training_args,\n",
|
|||
|
|
" compute_metrics=compute_metrics,\n",
|
|||
|
|
" train_dataset=train_data,\n",
|
|||
|
|
" eval_dataset=eval_data,\n",
|
|||
|
|
"\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"trainer.train()\n",
|
|||
|
|
"\n",
|
|||
|
|
"#for line in test_data:\n",
|
|||
|
|
"\n",
|
|||
|
|
"# inputs = tokenizer(line.get('text'), return_tensors=\"pt\")\n",
|
|||
|
|
"# labels = torch.tensor([1]).unsqueeze(0) # Batch size 1\n",
|
|||
|
|
"# outputs = model(**inputs, labels=labels)\n",
|
|||
|
|
"# _, predictions = torch.max(outputs[1], 1)\n",
|
|||
|
|
"# print(predictions)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"i = 0\n",
|
|||
|
|
"sum_preds = 0\n",
|
|||
|
|
"model = model.to('cpu')\n",
|
|||
|
|
"for line in test_data:\n",
|
|||
|
|
"\n",
|
|||
|
|
" inputs = tokenizer(line.get('text'), return_tensors=\"pt\")\n",
|
|||
|
|
" labels = torch.tensor([1]).unsqueeze(0) # Batch size 1\n",
|
|||
|
|
" outputs = model(**inputs, labels=labels)\n",
|
|||
|
|
" _, predictions = torch.max(outputs[1], 1)\n",
|
|||
|
|
" a = int(predictions.int())\n",
|
|||
|
|
" b = line.get('label')\n",
|
|||
|
|
" i += 1\n",
|
|||
|
|
" sum_preds += int(a == b)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(f\"ACCURACY: {(sum_preds/i * 100)}\")\n",
|
|||
|
|
"\n"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "Yo80KbX5gxVi",
|
|||
|
|
"outputId": "96ed92b0-bee3-4139-eeff-9f2c0b9c33fb"
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"ACCURACY: 93.15\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
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|
|
"colab": {
|
|||
|
|
"toc_visible": true,
|
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|
|
"provenance": []
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|
},
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|
"kernelspec": {
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|
"display_name": "Python 3",
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|
"name": "python3"
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|
|
},
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|
"language_info": {
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|
|
"name": "python"
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|
|
},
|
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|
|
"accelerator": "GPU",
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"gpuClass": "standard",
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"widgets": {
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