From bb2ace263e99c8ee6d3e99a5ad5f8cfdbb33c1f9 Mon Sep 17 00:00:00 2001 From: Jakub Pokrywka Date: Mon, 7 Jun 2021 15:13:35 +0200 Subject: [PATCH] transformers isi --- transformers.ipynb | 1157 +++++++++++++++++++++++++++++++++ transformers_ODPOWIEDZI.ipynb | 59 +- 2 files changed, 1169 insertions(+), 47 deletions(-) create mode 100644 transformers.ipynb diff --git a/transformers.ipynb b/transformers.ipynb new file mode 100644 index 0000000..e4d4614 --- /dev/null +++ b/transformers.ipynb @@ -0,0 +1,1157 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# bpe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "pip install tokenizers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://github.com/huggingface/tokenizers/tree/master/bindings/python" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from tokenizers import Tokenizer, models, trainers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tokenizers.trainers import BpeTrainer" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = Tokenizer(models.BPE())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = trainers.BpeTrainer(vocab_size=20000, min_frequency=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer.train(files = ['/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia9_ngramowy_model_jDDezykowy/pan-tadeusz-train.txt'], trainer = trainer)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "output = tokenizer.encode(\"Nie śpiewają piosenek: pracują leniwo,\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[236, 2255, 2069, 3898, 9908, 14, 8675, 8319, 191, 7]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.ids" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Nie', ' śpie', 'wają', ' pios', 'enek', ':', ' pracują', ' leni', 'wo', ',']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.tokens" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer.save(\"./my-bpe.tokenizer.json\", pretty=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ZADANIE\n", + "stworzyć BPE tokenizer na podstawie https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/train/train.tsv\n", + "i stworzyć stokenizowaną listę: \n", + "https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/test-A/in.tsv\n", + "\n", + "wybrać vocab_size = 8k, uwzględnić dodatkowe tokeny: BOS oraz EOS i wpleść je do zbioru testowego" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# transformery" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline, set_seed" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import RobertaTokenizer, RobertaModel" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "model = RobertaModel.from_pretrained('roberta-base')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"Replace me by any text you'd like. Bla Bla\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "encoded_input = tokenizer(text, return_tensors='pt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,\n", + " 102, 2091, 102, 2]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_input['input_ids']" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,\n", + " 102, 2091, 102, 2]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_input['input_ids']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' me'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.decode([162])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "output = model(**encoded_input)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[-4.4858e-02, 8.6642e-02, -7.2129e-03, ..., -4.6295e-02,\n", + " -3.9316e-02, 4.5264e-04],\n", + " [-6.0603e-02, 1.5684e-01, 4.3705e-02, ..., 5.3485e-01,\n", + " 8.4371e-02, 1.4826e-01],\n", + " [-2.3786e-02, -1.2086e-02, 7.8233e-02, ..., -4.9132e-01,\n", + " 1.2500e-01, 3.3293e-01],\n", + " ...,\n", + " [ 6.7192e-02, 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3.3103e-01, 4.8806e-02,\n", + " 9.9796e-02, -3.0042e-02, 1.4140e-01, 9.6566e-02, -1.1071e-01,\n", + " 2.7989e-01, 6.1347e-02, -5.6286e-01, -1.4422e-01, -2.1070e-01,\n", + " 7.6292e-02, 1.9691e-01, -3.3576e-01, 1.7638e-02, 2.2105e-02,\n", + " 1.4265e-01, 3.0694e-02, -1.0665e-01, -5.5197e-02, 3.9164e-01,\n", + " 2.0961e-01, 2.9841e-01, 8.7946e-02, 2.4076e-01, -1.0636e-02,\n", + " -3.3807e-01, 2.2974e-02, 8.5258e-02, -1.8663e-01, 4.1414e-01,\n", + " -9.9141e-02, -3.8117e-01, -5.6155e-02, 3.9692e-01, 9.7551e-02,\n", + " -1.8710e-02, -5.0913e-02, 2.0049e-01, 1.5407e-01, -1.2523e-01,\n", + " 1.8187e-01, -9.7470e-03, -1.3372e-01, -1.0178e-01, 8.5468e-02,\n", + " -2.1953e-01, 4.7566e-02, -1.3239e-01, 1.3200e-03, -2.0911e-01,\n", + " 3.2521e-03, -2.1387e-01, 2.4508e-01, -3.2182e-01, 9.8634e-02,\n", + " 7.5848e-02, 2.9231e-01, -3.5121e-01, -1.4159e-01, -5.7937e-02,\n", + " 1.6263e-01, 2.5830e-01, 3.5601e-01, 2.3997e-02, 2.5322e-02,\n", + " -1.5363e-01, -2.6361e-01, 5.4986e-02, -2.0897e-01, 1.2282e-01,\n", + " 7.1346e-02, 2.4762e-01, -3.0430e-01, -1.8016e-01, 2.2226e-01,\n", + " -9.7989e-02, -1.4158e-01, 4.2292e-01, 2.5139e-01, 2.1049e-01,\n", + " 2.2865e-02, 2.4210e-01, 3.7744e-02, -1.7568e-01, -1.1512e-01,\n", + " -2.4392e-01, 6.9097e-02, -8.5799e-02, -5.8893e-02, -7.1211e-02,\n", + " -1.2143e-01, -1.9825e-01, -1.5658e-01, 1.5637e-01, 1.3693e-01,\n", + " 2.9095e-02, -5.5552e-02, -2.4771e-02, -2.7771e-01, 2.9286e-01,\n", + " 2.4894e-02, 7.2069e-02, -4.8322e-02, 2.3967e-02, -1.5199e-01,\n", + " 2.3989e-01, 2.0234e-01, 8.2009e-02, -1.8899e-01, -4.8667e-02,\n", + " -2.9075e-01, -3.5470e-01, 4.1930e-02, 1.3129e-01, 1.1387e-01,\n", + " -1.0165e-01, -2.7247e-01, -2.7974e-02, -1.3051e-01, 1.8051e-01,\n", + " -9.6646e-03, -1.5500e-01, -7.4565e-02, -6.0039e-02, -5.1055e-02,\n", + " 6.7692e-02, -2.0781e-01, -1.9844e-01, -1.2495e-01, -7.5151e-02,\n", + " -6.6146e-02, 3.6196e-01, -3.5989e-02, 2.7737e-01, -1.5471e-01,\n", + " 1.1208e-02, -1.9818e-01, 1.0743e-01, -7.3001e-02, 7.3365e-02,\n", + " 2.6398e-01, -4.2969e-01, -1.5308e-01, 6.1186e-03, -2.1301e-01,\n", + " -1.4149e-01, -7.1113e-02, -4.0364e-02, 2.1242e-01, -3.4205e-01,\n", + " 2.1659e-01, -8.0915e-02, 1.8907e-01, -9.4013e-02, -2.5456e-01,\n", + " -1.6216e-01, 2.3130e-02, 2.4984e-01, -3.3239e-01, -2.2947e-01,\n", + " -2.6681e-01, -9.7903e-02, -9.0469e-02, -2.6217e-01, 4.1510e-01,\n", + " -1.0590e-01, -5.5713e-02, 9.9271e-03, 4.3321e-01, 1.9454e-01,\n", + " 1.5135e-01, 2.1670e-01, -1.3371e-02, 2.7091e-02, 1.0805e-01,\n", + " -4.6743e-01, 2.3397e-01, -2.2627e-01, -1.2724e-01, 2.7149e-02,\n", + " 8.9104e-02, -3.1547e-02, 1.2930e-02, -1.1888e-01, -1.0141e-01,\n", + " 2.0849e-01, -3.6962e-01, -1.2304e-02, 2.7230e-01, 1.4519e-01,\n", + " -2.4969e-01, 4.2865e-02, 1.2965e-01, 3.7797e-01, 8.8492e-02,\n", + " -2.2487e-01, 1.3100e-01, -3.4240e-01, -2.4896e-02, -1.8675e-01,\n", + " -2.9198e-01, 1.3836e-01, -6.9468e-02, 5.4983e-02, -6.8482e-02,\n", + " -2.7968e-01, 2.1223e-01, -5.0621e-02, -6.3859e-02, 4.1759e-01,\n", + " 3.3747e-02, -1.1644e-01, 1.5398e-01, 1.5137e-02, -5.4925e-03,\n", + " -1.0726e-01, 2.6553e-01, 2.0031e-01, -2.7755e-01, 1.2135e-01,\n", + " -1.2860e-01, -2.5987e-02, -1.1620e-01]], grad_fn=), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://huggingface.co/transformers/main_classes/output.html#basemodeloutputwithpoolingandcrossattentionsM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://arxiv.org/pdf/1907.11692.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 16, 768])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 768])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "output[1].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "output = model(**encoded_input, output_hidden_states=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[ 0.1664, -0.0541, -0.0014, ..., -0.0811, 0.0794, 0.0155],\n", + " [-0.7241, 0.1035, 0.0784, ..., 0.2474, -0.0535, 0.4320],\n", + " [ 0.5926, -0.1062, 0.0372, ..., -0.0140, 0.1021, -0.2212],\n", + " ...,\n", + " [ 0.4734, -0.0570, -0.2506, ..., 0.4071, 0.4481, -0.2180],\n", + " [ 0.7836, -0.2838, -0.2083, ..., -0.0959, -0.0136, 0.1995],\n", + " [ 0.2733, -0.1372, -0.0387, ..., 0.5187, 0.1545, -0.2604]]],\n", + " grad_fn=)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 16, 768])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 16, 768])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][1].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 16, 768])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][12].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "output = model(**encoded_input, output_attentions=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 12, 16, 16])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[[9.8775e-01, 6.2288e-04, 8.7264e-04, ..., 5.4309e-04,\n", + " 1.3059e-03, 1.0826e-03],\n", + " [3.3152e-01, 7.3213e-03, 3.0339e-02, ..., 1.6386e-03,\n", + " 1.1041e-03, 1.0450e-03],\n", + " [8.4058e-01, 7.4270e-04, 1.8587e-04, ..., 1.9484e-03,\n", + " 8.3106e-04, 2.2206e-03],\n", + " ...,\n", + " [8.3998e-01, 6.3201e-06, 7.9328e-06, ..., 1.8371e-02,\n", + " 5.9146e-02, 7.1377e-02],\n", + " [9.4819e-01, 3.9591e-06, 2.9191e-06, ..., 9.6707e-03,\n", + " 1.1201e-02, 2.4954e-02],\n", + " [9.2851e-01, 4.9144e-04, 2.2858e-04, ..., 9.3861e-03,\n", + " 1.7582e-02, 2.4180e-02]],\n", + "\n", + " [[9.2353e-01, 4.3481e-03, 1.9423e-02, ..., 5.0829e-03,\n", + " 7.5931e-03, 4.6599e-03],\n", + " [9.7840e-01, 4.1909e-03, 9.0263e-03, ..., 2.1102e-06,\n", + " 5.4437e-07, 6.7581e-06],\n", + " [8.3596e-01, 6.3265e-02, 7.9091e-02, ..., 1.4975e-05,\n", + " 2.1750e-06, 2.3804e-06],\n", + " ...,\n", + " [4.7469e-01, 1.1083e-04, 1.8293e-03, ..., 9.7021e-03,\n", + " 6.5544e-03, 1.9043e-03],\n", + " [2.1963e-01, 1.3427e-06, 1.2042e-04, ..., 7.5510e-01,\n", + " 2.8724e-03, 6.2941e-03],\n", + " [4.2043e-01, 3.4030e-06, 6.4028e-05, ..., 8.2335e-02,\n", + " 3.9994e-01, 9.1114e-02]],\n", + "\n", + " [[9.8968e-01, 2.9357e-04, 2.4483e-04, ..., 2.0526e-04,\n", + " 4.1698e-04, 3.3650e-03],\n", + " [9.0939e-01, 3.1261e-03, 2.7859e-02, ..., 3.1149e-04,\n", + " 8.0127e-05, 2.8887e-03],\n", + " [8.9282e-01, 2.4450e-04, 5.3892e-03, ..., 8.5178e-04,\n", + " 9.8922e-05, 2.7169e-03],\n", + " ...,\n", + " [9.3745e-01, 2.0096e-06, 4.1223e-06, ..., 4.7319e-02,\n", + " 3.8060e-03, 6.3264e-03],\n", + " [9.5799e-01, 1.2817e-04, 1.0723e-05, ..., 1.0232e-03,\n", + " 2.1168e-02, 3.7038e-03],\n", + " [9.1897e-01, 4.5952e-04, 7.4514e-05, ..., 5.2304e-05,\n", + " 3.8385e-05, 5.9209e-02]],\n", + "\n", + " ...,\n", + "\n", + " [[9.7214e-01, 1.8048e-03, 2.0910e-03, ..., 1.5654e-03,\n", + " 2.0380e-03, 2.9465e-03],\n", + " [2.0737e-01, 1.5373e-02, 3.4949e-01, ..., 1.0591e-04,\n", + " 3.8994e-06, 1.9794e-05],\n", + " [7.0131e-01, 2.8094e-03, 7.6395e-03, ..., 1.2338e-03,\n", + " 8.6231e-05, 8.1068e-05],\n", + " ...,\n", + " [4.1426e-01, 1.9507e-06, 5.5085e-05, ..., 3.8152e-02,\n", + " 4.5979e-01, 6.9998e-02],\n", + " [7.5517e-01, 2.2428e-07, 3.2856e-06, ..., 1.3153e-02,\n", + " 5.5085e-03, 2.1891e-01],\n", + " [9.4142e-01, 3.3256e-05, 6.0546e-06, ..., 9.1890e-04,\n", + " 8.7666e-03, 3.8735e-02]],\n", + "\n", + " [[9.7447e-01, 1.1291e-03, 2.3473e-03, ..., 1.6628e-03,\n", + " 1.7247e-03, 3.7978e-03],\n", + " [7.2027e-01, 5.4353e-02, 5.0394e-03, ..., 4.7070e-03,\n", + " 1.4477e-03, 7.9330e-02],\n", + " [9.1602e-01, 6.2537e-03, 6.2520e-03, ..., 3.0431e-03,\n", + " 1.6902e-03, 2.6523e-02],\n", + " ...,\n", + " [8.7035e-01, 5.6680e-03, 2.5519e-04, ..., 1.0693e-02,\n", + " 1.0154e-02, 2.8158e-02],\n", + " [7.8992e-01, 1.3184e-03, 5.2799e-04, ..., 3.8399e-03,\n", + " 2.3379e-02, 5.4757e-02],\n", + " [4.0584e-01, 5.6631e-03, 8.5153e-03, ..., 1.0006e-02,\n", + " 1.0799e-02, 1.9912e-01]],\n", + "\n", + " [[9.8713e-01, 3.3973e-04, 9.6788e-04, ..., 2.1040e-04,\n", + " 1.3595e-03, 8.0080e-04],\n", + " [1.0312e-01, 4.2905e-03, 8.3475e-01, ..., 7.3782e-06,\n", + " 1.9842e-04, 1.3445e-03],\n", + " [7.9036e-01, 2.8547e-02, 5.0725e-02, ..., 1.9356e-05,\n", + " 6.4891e-05, 2.8477e-03],\n", + " ...,\n", + " [2.1335e-01, 9.7233e-06, 6.9469e-05, ..., 3.6693e-04,\n", + " 3.3324e-01, 1.3384e-02],\n", + " [1.1667e-02, 3.0911e-05, 2.5899e-06, ..., 5.6125e-01,\n", + " 2.7517e-04, 1.5053e-03],\n", + " [8.4494e-01, 8.0791e-04, 1.0116e-03, ..., 2.4602e-03,\n", + " 6.7727e-02, 1.1728e-02]]]], grad_fn=)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[2][2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## gotowe api" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### generowanie tekstu" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of GPT2Model were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + } + ], + "source": [ + "model = pipeline('text-generation', model='gpt2')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Hello, I\\'m a computer science student by trade. I don\\'t really like science.\"\\n\\nThen I hear him say: \"I love the'},\n", + " {'generated_text': \"Hello, I'm a computer science student.\\n\\nAnd if you're curious what I'm doing here, don't hesitate:\\n\\n\\nI've\"},\n", + " {'generated_text': \"Hello, I'm a computer science student, not an engineer. But, I'm also fascinated, because all the people I'm talking to are engineers\"},\n", + " {'generated_text': \"Hello, I'm a computer science student with a big project called the Data Science project, to help students create and understand and improve their data science.\"},\n", + " {'generated_text': \"Hello, I'm a computer science student from North Carolina. My work involves a number of questions (and many possible answers as well). I can't\"}]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(\"Hello, I'm a computer science student\", max_length=30, num_return_sequences=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data. It is also working on creating a new type of\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big datasets. It has now given every request to Google for information\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data, robotics and artificial intelligence. We understand the potential impact\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data, artificial intelligence, and machine learning. I think we\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big new ways to see and interact with image data. We'll\"}]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(\"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big\", max_length=30, num_return_sequences=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### sentiment analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "\n", + "model = pipeline(\"sentiment-analysis\", model='distilbert-base-uncased-finetuned-sst-2-english')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'label': 'POSITIVE', 'score': 0.9998474717140198}]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(\"I'm very happy. Today is the beatifull weather\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'label': 'NEGATIVE', 'score': 0.9946851134300232}]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(\"It's raining. What a terrible day...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NER" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "model = pipeline(\"sentiment-analysis\", model='distilbert-base-uncased-finetuned-sst-2-english')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "model = pipeline(\"ner\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"George Washington went to Washington\"" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'word': 'George',\n", + " 'score': 0.9983943104743958,\n", + " 'entity': 'I-PER',\n", + " 'index': 1,\n", + " 'start': 0,\n", + " 'end': 6},\n", + " {'word': 'Washington',\n", + " 'score': 0.9992505311965942,\n", + " 'entity': 'I-PER',\n", + " 'index': 2,\n", + " 'start': 7,\n", + " 'end': 17},\n", + " {'word': 'Washington',\n", + " 'score': 0.98389732837677,\n", + " 'entity': 'I-LOC',\n", + " 'index': 5,\n", + " 'start': 26,\n", + " 'end': 36}]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### masked language modelling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ZADANIE (10 minut)\n", + "\n", + "przewidziać token w \"The world II started in 1939\"\" wg dowolnego anglojęzycznego modelu" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/transformers_ODPOWIEDZI.ipynb b/transformers_ODPOWIEDZI.ipynb index 652e6e4..40e4165 100644 --- a/transformers_ODPOWIEDZI.ipynb +++ b/transformers_ODPOWIEDZI.ipynb @@ -2736,13 +2736,6 @@ "output[1].shape" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 36, @@ -3103,11 +3096,11 @@ { "data": { "text/plain": [ - "[{'generated_text': 'Hello, I\\'m a computer science student in the computer sciences, and I am passionate about teaching web, Java, Python and more.\"\\n\\nK'},\n", - " {'generated_text': 'Hello, I\\'m a computer science student and a professor. I taught programming in my graduate classes. It\\'s not one of those \"I don\\'t'},\n", - " {'generated_text': \"Hello, I'm a computer science student at UCLA. After completing my MSc in Computer Science from UCLA in 2008, I took the position of computer\"},\n", - " {'generated_text': \"Hello, I'm a computer science student in California doing graduate work as a programmer in the tech sector. This is kind of my hobby, but I\"},\n", - " {'generated_text': \"Hello, I'm a computer science student, and my girlfriend is a software engineer. In short, I've just gone for a walk, and I\"}]" + "[{'generated_text': 'Hello, I\\'m a computer science student by trade. I don\\'t really like science.\"\\n\\nThen I hear him say: \"I love the'},\n", + " {'generated_text': \"Hello, I'm a computer science student.\\n\\nAnd if you're curious what I'm doing here, don't hesitate:\\n\\n\\nI've\"},\n", + " {'generated_text': \"Hello, I'm a computer science student, not an engineer. But, I'm also fascinated, because all the people I'm talking to are engineers\"},\n", + " {'generated_text': \"Hello, I'm a computer science student with a big project called the Data Science project, to help students create and understand and improve their data science.\"},\n", + " {'generated_text': \"Hello, I'm a computer science student from North Carolina. My work involves a number of questions (and many possible answers as well). I can't\"}]" ] }, "execution_count": 49, @@ -3136,11 +3129,11 @@ { "data": { "text/plain": [ - "[{'generated_text': 'I want to contribute to Google\\'s Computer Vision Program, which is doing extensive work on big data analytics.\"\\n\\n\"We want to accelerate this,\"'},\n", - " {'generated_text': 'I want to contribute to Google\\'s Computer Vision Program, which is doing extensive work on big data,\" she said.\\n\\nGoogle was founded by co'},\n", - " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data and machine learning. (To help in this effort,\"},\n", - " {'generated_text': 'I want to contribute to Google\\'s Computer Vision Program, which is doing extensive work on big data, artificial intelligence and other fields.\" But it\\'s not'},\n", - " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data visualizations and computational image recognition. I was fortunate enough\"}]" + "[{'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data. It is also working on creating a new type of\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big datasets. It has now given every request to Google for information\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data, robotics and artificial intelligence. We understand the potential impact\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data, artificial intelligence, and machine learning. I think we\"},\n", + " {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big new ways to see and interact with image data. We'll\"}]" ] }, "execution_count": 50, @@ -3178,7 +3171,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 52, @@ -3333,7 +3326,7 @@ } ], "source": [ - "model = pipeline(\"fill-mask\")\n" + "model = pipeline(\"fill-mask\")" ] }, { @@ -3374,34 +3367,6 @@ "source": [ "model(f\"The world {model.tokenizer.mask_token} II started in 1939\")\n" ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FillMaskPipeline' object has no attribute 'tokenize'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FillMaskPipeline' object has no attribute 'tokenize'" - ] - } - ], - "source": [ - "model.tokenize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {