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Diffstat (limited to 'fine_tune/bert/tutorials/01_mlm.ipynb')
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diff --git a/fine_tune/bert/tutorials/01_mlm.ipynb b/fine_tune/bert/tutorials/01_mlm.ipynb new file mode 100644 index 0000000..95ab5be --- /dev/null +++ b/fine_tune/bert/tutorials/01_mlm.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:45:18.997095Z", + "start_time": "2022-06-27T13:45:18.394848Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chunhuizhang/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/Users/chunhuizhang/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/Users/chunhuizhang/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n" + ] + } + ], + "source": [ + "from transformers import BertTokenizer, BertForMaskedLM\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:40:25.320915Z", + "start_time": "2022-06-27T13:40:25.318612Z" + } + }, + "outputs": [], + "source": [ + "model_name = 'bert-base-uncased'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:41:09.358613Z", + "start_time": "2022-06-27T13:40:57.925124Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n", + "- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" + ] + } + ], + "source": [ + "tokenizer = BertTokenizer.from_pretrained(model_name)\n", + "mlm = BertForMaskedLM.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:42:59.026410Z", + "start_time": "2022-06-27T13:42:59.020472Z" + } + }, + "outputs": [], + "source": [ + "with open('./data/text/meditations/clean.txt', 'r') as f:\n", + " texts = f.read().split('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:43:00.127330Z", + "start_time": "2022-06-27T13:43:00.123932Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "507" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(texts)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:44:00.414360Z", + "start_time": "2022-06-27T13:44:00.408114Z" + } + }, + "outputs": [], + "source": [ + "max_len = 0\n", + "text_len_list = []\n", + "for text in texts:\n", + " text_len = len(text.split(' '))\n", + " text_len_list.append(text_len)\n", + "# print(text_len)\n", + "# if text_len > max_len:\n", + "# max_len = text_len" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:43:18.412453Z", + "start_time": "2022-06-27T13:43:18.409195Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "880" + ] + }, + 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(cnt, bars, _) = plt.hist(text_len_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-27T13:49:10.416996Z", + "start_time": "2022-06-27T13:49:10.413790Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(bars)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |