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"_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } }, "gpuClass": "premium" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "2iYLQO5Evvqy" }, "source": [ "# Finetuning a Transformer model\n", "\n", "In this notebook, we train a **RoBERTa** model for sequence classification on a **sentiment analysis** task using HuggingFace's [transformers](https://github.com/huggingface/transformers) library.\n", "\n", "We do **full finetuning** of a pre-trained model using HuggingFace's setup here. Refer to [this notebook](https://colab.research.google.com/drive/1QR2Vy4mJFUi5r3HaQVROY3dQ9QMTJqhR?usp=sharing) for the same guide using _AdapterHub_ and Adapters.\n", "\n", "For training, we use the [movie review dataset by Pang and Lee (2005)](http://www.cs.cornell.edu/people/pabo/movie-review-data/). It contains movie reviews from Rotten Tomatoes which are either classified as positive or negative. We download the dataset via HuggingFace's [datasets](https://github.com/huggingface/datasets) library." ] }, { "cell_type": "markdown", "metadata": { "id": "a-XTIOLv0isn" }, "source": [ "## Installation\n", "\n", "First, let's install the required libraries:" ] }, { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_rDCu4VllIxC", "outputId": "515624ef-16b4-4686-a16d-aad3f11dcb04" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Thu Mar 16 22:15:42 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 32C P0 51W / 400W | 0MiB / 40960MiB | 0% Default |\n", "| | | Disabled |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "ju-alwbHmKYA", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "07a9770c-c417-4735-a0c2-570e14e9c637" }, "source": [ "!pip install transformers\n", "!pip install datasets" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, 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(from pandas->datasets) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.15.0)\n", "Installing collected packages: xxhash, multidict, frozenlist, dill, charset-normalizer, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n", "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 charset-normalizer-3.1.0 datasets-2.10.1 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "7Mx916lBCfoL" }, "source": [ "## Dataset Preprocessing\n", "\n", "Before we start to train our model, we first prepare the training data. Our training dataset can be loaded via HuggingFace `datasets` using one line of code:" ] }, { "cell_type": "code", "metadata": { "id": "DYHRQKkM5xhl", "colab": { "base_uri": "https://localhost:8080/", "height": 333, "referenced_widgets": [ "3c022ab85bcc4c748a11685ebc976edd", "f8b7b890b49f48498392c94b90ef9182", "9eeaafb51ef44948ab13ad117a0a05cd", "aa9e92a7dbf842a0926c24c3a826c070", "2efec0de4bce4a65ae3d7e1e658509a8", "1d51419bf90f4c9e9f1b9f2a13d9eee8", "1f08a9cb1eca449cb5d9b19077aba808", "4368537adae64d85af095c1b0d9ebb61", "c6663ef761194e1ca473adef1a14ba2c", "46b54c2c491843ae80f1b84cea19552f", "48bb3ab513714a818943ce2c8e2b58eb", "36ac9c587ef84b799a35813b4ed63093", "1d9657dc9da24accaebfe44748524064", "32a1c8b35d7a4abbb5b6558829a54a6d", "c05e868a402f4eca8ad356f7e20175cc", "b8e0921db4f24e5c9fb655ca1468f158", "ad78332aae1a42d2b2b5374cd696fb9e", "65699b255dad4c1791ce4558eb765923", "93a7c6a6b5cb41beaad0143a0734e1c1", "ae6ff0c10cbb44cbaf6bdc1e8533caa8", "c52d6ba54c8b4595a2c10b566aa8c034", "d9e07c17fcbb4551a7984479adac39ed", "6b4041380d5f4b7580b5906d478825fb", "4155d68a17e6482d9a177313e28a7de6", "0943301760fa402e80d201fa55683856", "ec593a78f6f4496b8616abdefe67afd8", "72525b665b914c3b9d8b5dda0524d5d4", "ae59728e975b4aa89c2456647218b818", "13e9dcdc34da4418a60e6b073f295557", "dccc83321a2142ba8d6667c8a98886bd", "70bcf34ad58a42d397db65d3e9711d2a", "44443f2d97d745449d3581463528d7fe", "17c541991dab4e2da11a79eebb225b2e", "732a7988575d49749ffaf558893a8126", "3d10f6decad048439e7602b13a974730", "386231bc68194fe7b3bc338cfb1a6926", "68a89b32ae934e64bac5778b4becafdb", "cba6c850e6bf4ebbb6698deea37abdac", "d49c2059076f48a680a7bce9b699d224", "735070cdce414f7299af29bc2d40fe57", "2a32323299654a25a1c32bc42b6d6e0d", "c9e878bfbc194048933154ab41bae1af", "648b02e8f67847d0a7b9771291fba172", "f22d38882aac4ee0bf7dc50ce1a4a8f4", "607c323447c6489b8afc24ca505ee0c9", "5ac9684847644be9b0c708f991daa652", "b537fab9928445f4bd0b3d5cd79246b3", "b57208e3ee334db89d27b4ff26cb7ffd", "47e5ba04c8644275b99d7c412e07ec24", "18dae821fca949428dfd52c4ba99c512", "d9b4b158b17444689766076a3a3ee406", "e6a68563e9a146c4b6c804ca291ad382", "71e4007820624b9a95813abfc2091582", "669dd24d10384ba9b2f089fbc2c4f213", "7ecb87b6dcaa41238b0214c9b8f82df0", "678c80c77d9e4bf182f2590e1a9bb36c", "8b0068b2a9da403791bc5e6a044db8a8", "0aa7950ac79649fa90dcf3573f91b86b", "0d09f9cc25674adcb25f092bf3538ca7", "48f9595fbcfd4b25884f4d738a05a0f5", "ae6921bd32fa407da3a673edc30bbb84", "77d850c734ed478d9e92a50bc1dda3e6", "c0908eaa35424b16b1b24017e9a58d4b", "ce00a6fde49d42b5b79775835079443d", "6f87e04711384b81a10cd7b49ef94b2a", "6ba31d28c22b40d99d6f4d5fa88864e4", "740713bdcd84470289d2d3a6ff4b7e93", "7c1558ac836649ac8f7893ee1d0b9688", "bca697bd69094bd29c7cc32e74119a64", "be4bb94b25564a5bb94ea067a9bb8952", "38572e8c9f974976927740cf0ea4ed68", "3de0f8bf09cf4fa5a2dfa1107d0a1214", "06242a1ddd5a4b919c3864444c4d9da5", "73a8fe434fa6475b833eac63a21b50ae", "e8f2668015f0469a96512a191422bbf2", "ad30c87d77f94b128a410671e570f19c", "6d56ade956ab4903980fca11be9b820f", "25ab6f3360b94243b524944568252c93", "93645363faab4eb289b6af778ec1059b", "620c1855c4f44711b0ca5a0c845f1e80", "ca310afcaa204f20a442652f152f948f", "08a3aa4c10784516806229e722a10994", "63745e3bfeb6498682ff6ed1cb706c0c", "48c823af5c924995b51cd2b9da169add", "97a6a27add674b43af75e838f5de4f56", "a1de114ec2a0446d94798c78f0ef4eb2", "e8dc4ade88384b64b69656093f2bdc12", "2d1c978d598c46deb59aba8d44f96e13" ] }, "outputId": "7233f878-c0b5-4593-ebc5-d5dd26c69d21" }, "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"rotten_tomatoes\")\n", "dataset.num_rows" ], "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/5.03k [00:00" ], "text/html": [ "\n", "
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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=30000, training_loss=0.6937461975097656, metrics={'train_runtime': 3753.0989, 'train_samples_per_second': 255.789, 'train_steps_per_second': 7.993, 'total_flos': 3.94021960954368e+16, 'train_loss': 0.6937461975097656, 'epoch': 112.36})" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "metadata": { "id": "KwyELxFKJJEz" }, "source": [ "Looks good! Let's evaluate our model on the validation split of the dataset to see how well it learned:" ] }, { "cell_type": "code", "metadata": { "id": "9PgHWEhsYeMv", "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "outputId": "432ef3bf-da9c-4c71-ef2d-11cc588dfe9b" }, "source": [ "trainer.evaluate()" ], "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [34/34 00:02]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'eval_loss': 0.6931476593017578,\n", " 'eval_acc': 0.5,\n", " 'eval_runtime': 2.8925,\n", " 'eval_samples_per_second': 368.54,\n", " 'eval_steps_per_second': 11.755,\n", " 'epoch': 112.36}" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "metadata": { "id": "PLLWSmCjJbVX" }, "source": [ "We can put our trained model into a `transformers` pipeline to be able to make new predictions conveniently:" ] }, { "cell_type": "code", "metadata": { "id": "Arw8GcsA8MeK", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "adeff136-bc97-4ac5-857f-d15d6de13e3f" }, "source": [ "from transformers import TextClassificationPipeline\n", "\n", "classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, device=training_args.device.index)\n", "\n", "classifier(\"I came across a lot of reviews stating that it is the best book out there.\")" ], "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[{'label': '👍', 'score': 0.500478208065033}]" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "markdown", "metadata": { "id": "cYykwoLaJvcJ" }, "source": [ "At last, we can also save the full model for later reuse:" ] }, { "cell_type": "code", "metadata": { "id": "ZfK_AtSz4tlt", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "27648d62-709b-4431-b89e-f94665e36905" }, "source": [ "model.save_pretrained(\"./final_model\")\n", "\n", "!ls -l final_model" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "total 486980\n", "-rw-r--r-- 1 root root 805 Mar 16 23:19 config.json\n", "-rw-r--r-- 1 root root 498662069 Mar 16 23:19 pytorch_model.bin\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 415, "referenced_widgets": [ "dd0fd19612bd40f585a1f8fdb4d00a4f", "d20061248fb24be2a1b242763138b021", "d06080401c784f778de67f2ff5143054", "eda09b3ce5164dac8bfe84ad5f1bf384", "b4d8646f7dc5479d89c596caf131f6b7", "a28f485799b34e58a8a85e743c9c7a4a", "238920073fef463a8acc8dc93202bede", "2384483e15bd4316bb293506bcb97c09", "a4a42660384946f992042645a738c4ad", "dc943607ee42406987500fe70ddbd4c5", "6a40fbdcd96444a99e9028a6d2f61ae3", "70a8c1abfcaf45b7bc97a3497a74ff16", "a0ca6e4d0d2e4eafa256c47533fb0cc2", "b597f0964a2f4680b0f55a45a66643f6", "d545dddffc7042adb83c2d2e296869f6", "9807f6b8658e4e7a9067311c4f568b12", "21bef08cb4874cc5a013ffbb866fab31" ] }, "id": "bPSIf6E2qZf_", "outputId": "b4141192-0ab1-4bd6-8ae7-c7184f586e0c" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Token is valid.\n", "Your token has been saved in your configured git credential helpers (store).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mGXwrOQSqZzj", "outputId": "c19d8266-c24d-44d1-9899-722e5b1e3b24" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "git-lfs is already the newest version (2.9.2-1).\n", "0 upgraded, 0 newly installed, 0 to remove and 23 not upgraded.\n" ] } ] }, { "cell_type": "code", "source": [ "!git config --global credential.helper store" ], "metadata": { "id": "SA9ivIrtqdQ1" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 436, "referenced_widgets": [ "9c00623e22af484ca452ef5cf7774393", "2efc4d417fca4ac48c05aad042dfdac8", "df4b0ed93cd443adbd02649ac1111745", "919ce8bc249f443f9d8b3bb29e425674", "f06adab4aace4a84b1b3b0e491934601", "8ddc5b952be848329891eff1fa507864", "a8adbae0217048c7bdb442739e0db230", "d22ee0c60b66447ebb24d19dc23b6ad1", "9d60b465fd4942108b7d543868bac2d0", "52af2a4ca9fe48c98eb3ccb9deb1b204", "627fa0bff1964353b819bf256f122dd4", "94dc33715db3444d80b189db16fa3334", "554a2fdb80b9493cb740689dd634cb33", "5c82844e99ff466e8c90c5df6af3fc35", "c5928281ba5b4406b207a026a2ecbbaf", "4a3e839e10064fe69576c0fd451ca61b", "8be8e05c1ec3417c9ec604ee90989c52", "cbb8d901b20748e0ad8948776d706b64", "089666bae03c47b5b3be123f6545adf4", "bcfe1591b91a45f2a9ada1885ddbc9f8", "8d1f3ea0e3044f94a8f8e13dbb122667", "490c6b16d9e54ead89797f9b59121a16" ] }, "id": "fKjz8xLcqhUo", "outputId": "5e167b3e-04e8-43c2-8007-62c9c8a84433" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Cloning https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1 into local empty directory.\n", "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1 into local empty directory.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file pytorch_model.bin: 0%| | 32.0k/476M [00:00 main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1\n", " 362539b..5e75779 main -> main\n", "\n", "To https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1\n", " 5e75779..d317b63 main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1\n", " 5e75779..d317b63 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/DrishtiSharma/roberta-base-rotten_tomatoes-v1/commit/5e75779da10986468c68430a1984c013eb840153'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "bdlAyDqBqjWe" }, "execution_count": null, "outputs": [] } ] }