{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "krQK4P9tWs_F" }, "source": [ "# 🧠 GPT-2 124M Fine-tuned on Wikitext\n", "\n", "This is an experiment aiming to achieve practical understanding of learning 🤗 Transformers and 🤗 Datasets. To follow the guide outlined [Hugging Face Notebooks](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9-IVR9eMWY8v", "outputId": "a2ebf62e-55b2-488e-95c8-b1ee10026308" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-2.15.0-py3-none-any.whl.metadata (20 kB)\n", "Collecting transformers\n", " Downloading transformers-4.35.2-py3-none-any.whl.metadata (123 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m123.5/123.5 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n", "\u001b[?25hCollecting accelerate\n", " Downloading accelerate-0.25.0-py3-none-any.whl.metadata (18 kB)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) 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multiprocess, huggingface-hub, aiosignal, tokenizers, aiohttp, accelerate, transformers, datasets\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2023.4.0\n", " Uninstalling fsspec-2023.4.0:\n", " Successfully uninstalled fsspec-2023.4.0\n", "Successfully installed accelerate-0.25.0 aiohttp-3.9.1 aiosignal-1.3.1 async-timeout-4.0.3 datasets-2.15.0 dill-0.3.7 frozenlist-1.4.0 fsspec-2023.10.0 huggingface-hub-0.19.4 multidict-6.0.4 multiprocess-0.70.15 pandas-2.1.3 pyarrow-14.0.1 pyarrow-hotfix-0.6 pytz-2023.3.post1 regex-2023.10.3 safetensors-0.4.1 tokenizers-0.15.0 tqdm-4.66.1 transformers-4.35.2 tzdata-2023.3 xxhash-3.4.1 yarl-1.9.3\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ "# Install neccessary packages\n", "!pip install datasets transformers accelerate" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "72d3f33c56a14b01bef05ea2ae98e72f", "8d44cf16b34d40bd8702560ae149192c", "13af4957747d40bbb9501bc13c3c160b", "1be0b602c74f4a7182bd041aeba58112", "af225dc2d79649c2ac0e853c517a0482", "f019d8eee73f4b7b8312029f03040c45", "d1deb908be244cfb8dd253b3cc081510", "06e659f7bd9149e784a5cb68efbad736", "ba1f1326e12d437a9fd76faa226eec44", "65ea346ce3174bb098344531db439eac", "6ac7576be7204aeca73385c48e1c5d0a", "7fd3c4de83234008a72d9541c29ce765", "b81585732ca14c89ae1b5bb25735cd62", "a68a4415305045c0a89c60349175cc82", "7316ece4ad474193bcc5b5db55632561", "c0bb9ace36d64b8296bd35d538bd4f7e", "03ad39c22f644e55bcffa070e3832582" ] }, "id": "lpuAz9I-XsQQ", "outputId": "bf420c42-7ff0-4a4c-da57-420691eaec5d" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e61086bda2ca40b5a189544f6a8b43a5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
1024). Running this sequence through the model will result in indexing errors\n", "Token indices sequence length is longer than the specified maximum sequence length for this model (1063 > 1024). Running this sequence through the model will result in indexing errors\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "43a4cddf296748669540d04b6ea1ca98", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=4): 0%| | 0/3760 [00:00\n", " \n", " \n", " [172401/172401 4:00:07, Epoch 3/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation Loss
13.1335003.036319
23.0643002.996815
33.0384002.984082

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