46 KiB
46 KiB
Ekstrakcja informacji
12. Transformery [ćwiczenia]
Jakub Pokrywka (2021)
bpe
pip install tokenizers
from tokenizers import Tokenizer, models, trainersfrom tokenizers.trainers import BpeTrainertokenizer = Tokenizer(models.BPE())trainer = trainers.BpeTrainer(vocab_size=20000, min_frequency=2)tokenizer.train(files = ['./pan-tadeusz-train.txt'], trainer = trainer)output = tokenizer.encode("Nie śpiewają piosenek: pracują leniwo,")output.ids[236, 2255, 2069, 3898, 9908, 14, 8675, 8319, 191, 7]
output.tokens['Nie', ' śpie', 'wają', ' pios', 'enek', ':', ' pracują', ' leni', 'wo', ',']
tokenizer.save("./my-bpe.tokenizer.json", pretty=True)ZADANIE
stworzyć BPE tokenizer na podstawie https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/train/train.tsv i stworzyć stokenizowaną listę: https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/test-A/in.tsv
wybrać vocab_size = 8k, uwzględnić dodatkowe tokeny: BOS oraz EOS i wpleść je do zbioru testowego
transformery
pip install transformershuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Requirement already satisfied: transformers in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (4.19.2) Requirement already satisfied: numpy>=1.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (1.22.3) Requirement already satisfied: filelock in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (3.7.0) Requirement already satisfied: pyyaml>=5.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (6.0) Requirement already satisfied: regex!=2019.12.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (2022.4.24) Requirement already satisfied: requests in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (2.27.1) Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (0.12.1) Requirement already satisfied: tqdm>=4.27 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (4.64.0) Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (0.6.0) Requirement already satisfied: packaging>=20.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (21.3) Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from packaging>=20.0->transformers) (3.0.8) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (1.26.9) Requirement already satisfied: idna<4,>=2.5 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (3.3) Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (2020.6.20) Requirement already satisfied: charset-normalizer~=2.0.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (2.0.4) Note: you may need to restart the kernel to use updated packages.
przykłady pochodzą częściowo z: https://huggingface.co/
import torchfrom transformers import pipeline, set_seedfrom transformers import RobertaTokenizer, RobertaModeltokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight'] - This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
text = "Replace me by any text you'd like. Bla Bla"encoded_input = tokenizer(text, return_tensors='pt')encoded_input['input_ids']tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,
102, 2091, 102, 2]])encoded_input['input_ids']tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,
102, 2091, 102, 2]])tokenizer.decode([162])' me'
output = model(**encoded_input)outputBaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[-4.4858e-02, 8.6642e-02, -7.2130e-03, ..., -4.6295e-02,
-3.9316e-02, 4.5262e-04],
[-6.0603e-02, 1.5684e-01, 4.3704e-02, ..., 5.3484e-01,
8.4371e-02, 1.4826e-01],
[-2.3786e-02, -1.2086e-02, 7.8232e-02, ..., -4.9132e-01,
1.2500e-01, 3.3293e-01],
...,
[ 6.7191e-02, 2.4028e-01, -2.9984e-01, ..., 2.1992e-01,
1.9186e-02, 1.5355e-01],
[ 1.7611e-01, 1.4001e-01, -1.3774e-01, ..., -5.0379e-01,
7.3958e-02, 3.7871e-02],
[-3.4404e-02, 8.7647e-02, -3.9429e-02, ..., -9.1916e-02,
-3.5529e-02, -2.6777e-02]]], grad_fn=<NativeLayerNormBackward0>), pooler_output=tensor([[ 5.1994e-03, -2.1290e-01, -2.2585e-01, -9.3315e-02, 1.1761e-01,
1.9024e-01, 2.4873e-01, -8.1097e-02, -4.4050e-02, -1.6596e-01,
2.1572e-01, -8.3736e-03, -7.7416e-02, 8.2714e-02, -1.2487e-01,
4.8405e-01, 2.1145e-01, -4.4654e-01, 4.1008e-02, -1.2578e-02,
-2.5560e-01, 7.3874e-02, 4.6924e-01, 3.2284e-01, 1.2382e-01,
6.3117e-02, -1.2633e-01, -1.3542e-02, 1.6195e-01, 2.1738e-01,
2.8682e-01, 6.2675e-02, 8.5778e-02, 2.3686e-01, -2.5219e-01,
4.3382e-02, -3.0992e-01, 4.0551e-02, 2.3078e-01, -1.8763e-01,
-7.2085e-02, 1.5847e-01, 2.0228e-01, -1.2756e-01, -1.1429e-01,
3.9603e-01, 2.6018e-01, 2.9297e-02, -1.2162e-01, -8.2051e-02,
-3.5665e-01, 3.4722e-01, 2.8292e-01, 1.9929e-01, -1.4734e-02,
4.8892e-02, -1.4596e-01, 2.5527e-01, -7.5540e-02, -8.7507e-02,
-1.2164e-01, -2.0039e-01, -7.1404e-03, -5.9407e-02, 4.1914e-02,
-1.3208e-01, 6.8612e-02, -1.4154e-01, -1.1040e-01, 5.3550e-02,
-7.3125e-02, 1.6401e-01, 1.6232e-01, -3.0164e-01, -2.8429e-01,
5.9359e-02, -5.7466e-01, -9.3444e-02, 3.1263e-01, 4.3317e-01,
-1.0505e-01, 2.0451e-01, 3.2512e-02, 2.0723e-01, -2.0692e-02,
-7.1687e-02, -4.7481e-02, -9.9879e-02, 1.8269e-01, 2.6383e-01,
-1.8467e-01, -3.7178e-01, 7.7521e-02, 2.5127e-02, -1.0401e-01,
3.6107e-03, -2.1521e-02, -8.3309e-02, -1.6115e-01, -1.6062e-01,
4.8140e-02, -2.4970e-01, -1.4153e-01, 2.7024e-01, -3.3932e-02,
-2.0374e-01, -2.1484e-02, 2.9751e-01, 7.9157e-02, -1.1653e-01,
-1.9188e-01, 4.2722e-01, 2.7925e-01, 2.1393e-03, -1.2266e-02,
1.6474e-01, 1.5236e-01, -2.8896e-01, 4.2948e-01, -3.1129e-01,
-8.3018e-03, -1.2746e-01, 9.3701e-02, 1.3876e-01, -2.2297e-01,
2.7802e-01, 1.4876e-01, 2.7402e-01, 1.8048e-01, 1.0370e-01,
-1.9344e-02, 1.5151e-01, -1.0991e-01, 1.4710e-01, 2.1547e-01,
1.1827e-01, -3.4855e-03, -3.2536e-01, -2.0146e-01, 2.6933e-01,
3.2889e-01, 1.6086e-01, -4.0214e-02, 1.7948e-01, 9.3386e-02,
2.3509e-01, 1.3713e-01, -3.9689e-01, 2.7102e-02, 3.2785e-01,
9.5205e-02, 1.6616e-01, -8.5767e-02, -2.9547e-01, -2.5672e-01,
-9.8167e-02, 4.9819e-02, -3.1859e-01, -1.0416e-01, 3.6841e-01,
3.9702e-02, 1.0092e-02, -1.5538e-01, -2.3540e-01, -3.1184e-02,
-1.0212e-01, 2.2566e-02, 8.2764e-02, -8.8543e-02, -4.3323e-01,
-9.3633e-02, -5.2647e-01, -1.1645e-01, 1.9042e-01, -3.2305e-01,
2.3381e-01, -2.9311e-01, 9.7979e-02, 3.9168e-01, 4.9259e-02,
-1.4776e-03, -1.9380e-01, -2.3509e-02, 1.1105e-01, 3.2593e-01,
2.3894e-01, -4.0230e-01, 1.0931e-01, 1.4188e-01, 2.5775e-01,
1.4593e-01, -6.1273e-02, -1.1612e-01, 1.5098e-01, -2.0754e-01,
1.7434e-01, -2.2652e-01, 1.8130e-01, -2.4506e-01, -2.1763e-01,
2.7327e-01, -4.0463e-01, -3.2816e-02, 8.4528e-02, 2.6249e-01,
5.3813e-03, -3.7149e-02, -8.5346e-02, 1.2447e-01, 1.6730e-01,
1.2632e-01, -3.9535e-01, 2.6029e-01, -2.7662e-02, -1.0249e-02,
-3.3478e-02, 1.7810e-01, 2.5045e-01, 8.5239e-02, -3.8860e-01,
-1.2383e-01, 1.0050e-01, 2.8403e-01, -2.3275e-01, 1.4317e-01,
-2.5891e-01, -3.7883e-01, -1.4209e-01, 2.0352e-01, 2.2653e-01,
1.7335e-01, -2.7438e-01, 1.6319e-01, -1.0932e-01, -4.1693e-01,
-3.6249e-01, -1.0035e-01, 2.3360e-01, 1.6881e-01, 1.8671e-01,
2.4667e-01, 3.3260e-02, 1.0321e-01, 1.5088e-01, 1.4948e-01,
-1.4621e-01, 1.5615e-01, -3.4600e-01, -4.0963e-02, -2.5380e-01,
-1.9230e-01, -2.1718e-01, 3.9236e-01, -2.2989e-01, 2.3594e-01,
3.7562e-01, -3.1114e-01, -1.2649e-01, 1.6051e-01, 1.0577e-01,
8.3363e-02, -1.2593e-01, 2.0584e-01, 1.4127e-01, -1.0632e-01,
2.3048e-01, -9.1782e-03, 2.4953e-01, 1.7332e-01, 8.9003e-02,
1.5702e-01, 1.1363e-01, -1.4771e-01, 3.8719e-02, 3.1532e-03,
-1.9275e-02, -2.3103e-01, -1.4421e-01, 2.3575e-01, -5.3696e-02,
3.7994e-02, -1.6641e-01, -1.1399e-01, 1.1890e-02, 3.9652e-01,
-3.6048e-01, 2.4448e-01, 7.5291e-02, 1.5195e-01, -2.2864e-01,
-2.1031e-01, 8.9896e-02, 1.6992e-01, -3.9863e-01, 1.7434e-03,
1.6546e-01, 1.0483e-01, 2.1218e-01, 2.8159e-01, -5.0729e-05,
-9.4031e-02, 4.9128e-01, -1.6932e-01, -1.2761e-01, 2.5249e-01,
-2.7310e-01, -2.7744e-01, 2.4945e-01, -2.9252e-02, 3.0148e-01,
1.1743e-01, 3.8095e-02, 6.4731e-02, -6.0554e-01, 6.6722e-02,
-4.5959e-01, -8.5795e-03, 3.6827e-02, -7.1025e-02, -2.0848e-01,
1.4228e-01, 2.9630e-01, -2.4077e-01, -4.3098e-02, 2.2334e-01,
8.4614e-02, -1.2553e-01, 4.8810e-01, 1.4034e-03, 2.0182e-01,
-6.3799e-02, 2.4192e-01, -2.0799e-01, 2.6687e-01, -2.7694e-01,
-9.9754e-02, 4.7168e-03, 8.3846e-02, 4.5165e-02, -5.9169e-02,
-3.5243e-01, 2.2125e-01, -1.9798e-02, -5.7139e-02, -4.0613e-02,
9.3967e-02, -1.7488e-02, 6.2663e-02, 5.2265e-02, 3.4857e-01,
2.2626e-01, -2.6472e-02, -3.7240e-01, -1.1370e-02, -9.6964e-02,
5.6610e-02, 2.6788e-02, -2.4786e-02, 4.3236e-01, -7.6491e-02,
5.2428e-03, -1.4826e-01, 2.6029e-01, 1.8327e-01, 1.2439e-01,
1.3042e-01, 6.1801e-02, 1.3667e-01, -6.7373e-02, -1.2048e-02,
-1.4603e-01, -2.2505e-01, -2.9760e-01, 2.0056e-01, -2.2011e-01,
-1.8115e-01, 1.4262e-01, 2.1523e-01, -1.3893e-01, 1.4466e-01,
2.9357e-01, 1.2101e-01, -1.4499e-01, 2.6571e-01, -1.0101e-01,
1.1599e-01, 2.9781e-01, -2.0156e-02, 2.0140e-01, 5.0007e-01,
2.1717e-01, -3.5394e-01, -1.6840e-02, -2.2424e-01, 1.1153e-02,
2.4465e-01, -1.5139e-01, 1.9410e-01, 3.8725e-01, 2.9424e-01,
4.2920e-01, -7.3522e-03, -1.1083e-01, 9.0861e-02, 2.2493e-01,
2.7805e-02, -1.5957e-01, -1.9878e-01, 2.5621e-01, 6.2884e-02,
-1.5698e-01, -1.3003e-02, -1.1539e-01, 2.8383e-02, -1.2329e-01,
-3.8461e-01, 3.5215e-02, 1.8146e-01, -4.7366e-01, 7.3029e-02,
-2.9380e-01, 4.3577e-02, -2.3151e-01, 2.0148e-01, -2.2549e-01,
-1.0662e-01, 3.9916e-01, -7.4222e-02, 4.3181e-02, -1.7905e-01,
-1.3722e-01, 2.4749e-02, 7.4730e-03, -1.4543e-02, -4.1486e-03,
3.3529e-01, -1.2101e-01, 3.6759e-02, 3.6844e-02, 1.9582e-01,
-5.1381e-02, 2.0516e-01, 3.1175e-02, -1.4019e-01, -4.0386e-01,
1.4271e-01, -1.8793e-01, -4.2023e-01, -3.6638e-01, 3.6652e-01,
-1.3753e-01, -2.5359e-01, -2.0423e-01, -2.4466e-01, 8.1067e-02,
1.6987e-01, 4.7120e-01, -3.9858e-01, -6.8325e-02, 4.7077e-01,
-6.8745e-02, -1.8953e-01, 2.7360e-01, 1.8793e-01, -3.3325e-01,
3.1144e-01, 2.6919e-01, -5.6080e-02, 1.5771e-02, 5.0668e-01,
1.1729e-01, 1.8437e-01, -2.0954e-01, 4.4338e-01, -2.1112e-01,
3.1039e-01, -1.6460e-01, -2.1319e-01, -2.1592e-01, -1.9942e-02,
3.3144e-01, 1.8923e-01, -4.2029e-01, -1.0169e-01, 3.1353e-02,
3.6021e-01, -3.7626e-01, -8.6387e-02, 1.3697e-02, -3.3636e-01,
1.2770e-01, 1.0668e-01, 2.2197e-01, -3.7968e-01, -1.5053e-02,
3.9753e-01, -2.9535e-01, 1.3459e-01, 3.2518e-01, 7.6786e-02,
3.4168e-01, -2.8172e-02, 1.0189e-02, 5.9536e-02, -2.3156e-01,
-3.8199e-02, 1.3041e-01, 5.4866e-01, 1.5127e-01, -3.6896e-01,
9.5292e-02, 2.4462e-01, -1.6506e-01, 3.1529e-01, -8.9681e-02,
-4.6637e-02, 2.6508e-01, -3.6751e-02, 1.5445e-01, -9.7824e-02,
-2.1623e-01, -3.0666e-01, 3.6944e-01, -1.8711e-01, -1.1481e-01,
-1.6787e-01, -1.1253e-01, -1.4680e-01, 4.1271e-02, -3.6980e-01,
3.3081e-01, 1.2455e-01, -1.8123e-01, -6.8767e-02, -9.6390e-02,
-1.4910e-01, -2.0524e-01, -2.6686e-01, 4.2154e-01, -1.6543e-01,
-4.5050e-01, 2.5019e-01, 3.4722e-02, 3.3103e-01, 4.8806e-02,
9.9796e-02, -3.0042e-02, 1.4140e-01, 9.6566e-02, -1.1071e-01,
2.7989e-01, 6.1347e-02, -5.6286e-01, -1.4422e-01, -2.1070e-01,
7.6292e-02, 1.9691e-01, -3.3576e-01, 1.7638e-02, 2.2105e-02,
1.4265e-01, 3.0694e-02, -1.0665e-01, -5.5197e-02, 3.9164e-01,
2.0961e-01, 2.9841e-01, 8.7946e-02, 2.4076e-01, -1.0636e-02,
-3.3807e-01, 2.2974e-02, 8.5258e-02, -1.8663e-01, 4.1414e-01,
-9.9141e-02, -3.8117e-01, -5.6155e-02, 3.9692e-01, 9.7551e-02,
-1.8710e-02, -5.0913e-02, 2.0049e-01, 1.5407e-01, -1.2523e-01,
1.8187e-01, -9.7470e-03, -1.3372e-01, -1.0178e-01, 8.5468e-02,
-2.1953e-01, 4.7566e-02, -1.3239e-01, 1.3199e-03, -2.0911e-01,
3.2521e-03, -2.1387e-01, 2.4508e-01, -3.2182e-01, 9.8634e-02,
7.5848e-02, 2.9231e-01, -3.5121e-01, -1.4159e-01, -5.7937e-02,
1.6263e-01, 2.5830e-01, 3.5601e-01, 2.3997e-02, 2.5322e-02,
-1.5363e-01, -2.6361e-01, 5.4986e-02, -2.0897e-01, 1.2282e-01,
7.1346e-02, 2.4762e-01, -3.0430e-01, -1.8016e-01, 2.2226e-01,
-9.7989e-02, -1.4158e-01, 4.2292e-01, 2.5139e-01, 2.1049e-01,
2.2865e-02, 2.4210e-01, 3.7744e-02, -1.7568e-01, -1.1512e-01,
-2.4392e-01, 6.9097e-02, -8.5799e-02, -5.8893e-02, -7.1211e-02,
-1.2143e-01, -1.9825e-01, -1.5658e-01, 1.5637e-01, 1.3693e-01,
2.9095e-02, -5.5552e-02, -2.4771e-02, -2.7771e-01, 2.9286e-01,
2.4894e-02, 7.2069e-02, -4.8322e-02, 2.3967e-02, -1.5199e-01,
2.3989e-01, 2.0234e-01, 8.2009e-02, -1.8899e-01, -4.8667e-02,
-2.9075e-01, -3.5470e-01, 4.1930e-02, 1.3129e-01, 1.1387e-01,
-1.0165e-01, -2.7247e-01, -2.7974e-02, -1.3051e-01, 1.8051e-01,
-9.6647e-03, -1.5500e-01, -7.4565e-02, -6.0039e-02, -5.1055e-02,
6.7692e-02, -2.0781e-01, -1.9844e-01, -1.2495e-01, -7.5151e-02,
-6.6146e-02, 3.6196e-01, -3.5989e-02, 2.7737e-01, -1.5471e-01,
1.1208e-02, -1.9818e-01, 1.0743e-01, -7.3001e-02, 7.3365e-02,
2.6398e-01, -4.2969e-01, -1.5308e-01, 6.1187e-03, -2.1301e-01,
-1.4149e-01, -7.1113e-02, -4.0364e-02, 2.1242e-01, -3.4205e-01,
2.1659e-01, -8.0915e-02, 1.8907e-01, -9.4013e-02, -2.5456e-01,
-1.6216e-01, 2.3130e-02, 2.4984e-01, -3.3239e-01, -2.2947e-01,
-2.6681e-01, -9.7903e-02, -9.0469e-02, -2.6217e-01, 4.1510e-01,
-1.0590e-01, -5.5713e-02, 9.9270e-03, 4.3321e-01, 1.9454e-01,
1.5135e-01, 2.1670e-01, -1.3371e-02, 2.7091e-02, 1.0805e-01,
-4.6744e-01, 2.3397e-01, -2.2627e-01, -1.2724e-01, 2.7149e-02,
8.9104e-02, -3.1547e-02, 1.2930e-02, -1.1888e-01, -1.0141e-01,
2.0849e-01, -3.6962e-01, -1.2304e-02, 2.7230e-01, 1.4519e-01,
-2.4969e-01, 4.2865e-02, 1.2965e-01, 3.7797e-01, 8.8492e-02,
-2.2487e-01, 1.3100e-01, -3.4240e-01, -2.4896e-02, -1.8675e-01,
-2.9198e-01, 1.3836e-01, -6.9468e-02, 5.4983e-02, -6.8482e-02,
-2.7968e-01, 2.1223e-01, -5.0621e-02, -6.3859e-02, 4.1759e-01,
3.3747e-02, -1.1644e-01, 1.5398e-01, 1.5137e-02, -5.4926e-03,
-1.0726e-01, 2.6553e-01, 2.0031e-01, -2.7755e-01, 1.2135e-01,
-1.2860e-01, -2.5987e-02, -1.1620e-01]], grad_fn=<TanhBackward0>), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)len(output)2
output[0].shapetorch.Size([1, 16, 768])
output[1].shapetorch.Size([1, 768])
output = model(**encoded_input, output_hidden_states=True)len(output)3
len(output[2])13
output[2][0]tensor([[[ 0.1664, -0.0541, -0.0014, ..., -0.0811, 0.0794, 0.0155],
[-0.7241, 0.1035, 0.0784, ..., 0.2474, -0.0535, 0.4320],
[ 0.5926, -0.1062, 0.0372, ..., -0.0140, 0.1021, -0.2212],
...,
[ 0.4734, -0.0570, -0.2506, ..., 0.4071, 0.4481, -0.2180],
[ 0.7836, -0.2838, -0.2083, ..., -0.0959, -0.0136, 0.1995],
[ 0.2733, -0.1372, -0.0387, ..., 0.5187, 0.1545, -0.2604]]],
grad_fn=<NativeLayerNormBackward0>)output[2][0].shapetorch.Size([1, 16, 768])
output[2][1].shapetorch.Size([1, 16, 768])
output[2][12].shapetorch.Size([1, 16, 768])
output = model(**encoded_input, output_attentions=True)len(output)3
len(output[2])12
output[2][0].shapetorch.Size([1, 12, 16, 16])
output[2][2]tensor([[[[9.8775e-01, 6.2288e-04, 8.7263e-04, ..., 5.4309e-04,
1.3059e-03, 1.0826e-03],
[3.3152e-01, 7.3213e-03, 3.0339e-02, ..., 1.6386e-03,
1.1041e-03, 1.0450e-03],
[8.4058e-01, 7.4270e-04, 1.8587e-04, ..., 1.9484e-03,
8.3106e-04, 2.2206e-03],
...,
[8.3998e-01, 6.3201e-06, 7.9327e-06, ..., 1.8371e-02,
5.9146e-02, 7.1376e-02],
[9.4819e-01, 3.9591e-06, 2.9191e-06, ..., 9.6707e-03,
1.1201e-02, 2.4954e-02],
[9.2851e-01, 4.9144e-04, 2.2858e-04, ..., 9.3861e-03,
1.7582e-02, 2.4180e-02]],
[[9.2353e-01, 4.3481e-03, 1.9423e-02, ..., 5.0829e-03,
7.5931e-03, 4.6599e-03],
[9.7840e-01, 4.1908e-03, 9.0262e-03, ..., 2.1102e-06,
5.4436e-07, 6.7581e-06],
[8.3596e-01, 6.3264e-02, 7.9091e-02, ..., 1.4975e-05,
2.1750e-06, 2.3803e-06],
...,
[4.7469e-01, 1.1083e-04, 1.8293e-03, ..., 9.7021e-03,
6.5544e-03, 1.9043e-03],
[2.1963e-01, 1.3427e-06, 1.2042e-04, ..., 7.5510e-01,
2.8724e-03, 6.2941e-03],
[4.2043e-01, 3.4030e-06, 6.4028e-05, ..., 8.2335e-02,
3.9994e-01, 9.1114e-02]],
[[9.8968e-01, 2.9357e-04, 2.4483e-04, ..., 2.0526e-04,
4.1698e-04, 3.3650e-03],
[9.0939e-01, 3.1261e-03, 2.7859e-02, ..., 3.1149e-04,
8.0126e-05, 2.8887e-03],
[8.9282e-01, 2.4450e-04, 5.3892e-03, ..., 8.5178e-04,
9.8921e-05, 2.7168e-03],
...,
[9.3745e-01, 2.0096e-06, 4.1222e-06, ..., 4.7319e-02,
3.8060e-03, 6.3264e-03],
[9.5799e-01, 1.2817e-04, 1.0723e-05, ..., 1.0232e-03,
2.1167e-02, 3.7038e-03],
[9.1897e-01, 4.5951e-04, 7.4513e-05, ..., 5.2304e-05,
3.8385e-05, 5.9209e-02]],
...,
[[9.7214e-01, 1.8048e-03, 2.0910e-03, ..., 1.5654e-03,
2.0380e-03, 2.9465e-03],
[2.0737e-01, 1.5373e-02, 3.4949e-01, ..., 1.0591e-04,
3.8994e-06, 1.9794e-05],
[7.0131e-01, 2.8094e-03, 7.6395e-03, ..., 1.2338e-03,
8.6230e-05, 8.1068e-05],
...,
[4.1426e-01, 1.9507e-06, 5.5085e-05, ..., 3.8152e-02,
4.5979e-01, 6.9998e-02],
[7.5517e-01, 2.2428e-07, 3.2856e-06, ..., 1.3153e-02,
5.5085e-03, 2.1890e-01],
[9.4142e-01, 3.3256e-05, 6.0545e-06, ..., 9.1890e-04,
8.7666e-03, 3.8734e-02]],
[[9.7447e-01, 1.1291e-03, 2.3473e-03, ..., 1.6628e-03,
1.7247e-03, 3.7978e-03],
[7.2027e-01, 5.4353e-02, 5.0394e-03, ..., 4.7070e-03,
1.4477e-03, 7.9330e-02],
[9.1602e-01, 6.2537e-03, 6.2520e-03, ..., 3.0431e-03,
1.6902e-03, 2.6523e-02],
...,
[8.7035e-01, 5.6680e-03, 2.5519e-04, ..., 1.0693e-02,
1.0154e-02, 2.8158e-02],
[7.8992e-01, 1.3184e-03, 5.2799e-04, ..., 3.8399e-03,
2.3379e-02, 5.4757e-02],
[4.0584e-01, 5.6631e-03, 8.5153e-03, ..., 1.0006e-02,
1.0799e-02, 1.9912e-01]],
[[9.8713e-01, 3.3973e-04, 9.6788e-04, ..., 2.1040e-04,
1.3595e-03, 8.0080e-04],
[1.0312e-01, 4.2905e-03, 8.3475e-01, ..., 7.3782e-06,
1.9842e-04, 1.3445e-03],
[7.9036e-01, 2.8547e-02, 5.0725e-02, ..., 1.9356e-05,
6.4892e-05, 2.8477e-03],
...,
[2.1334e-01, 9.7233e-06, 6.9468e-05, ..., 3.6693e-04,
3.3324e-01, 1.3384e-02],
[1.1667e-02, 3.0911e-05, 2.5899e-06, ..., 5.6125e-01,
2.7517e-04, 1.5053e-03],
[8.4494e-01, 8.0791e-04, 1.0116e-03, ..., 2.4602e-03,
6.7727e-02, 1.1728e-02]]]], grad_fn=<SoftmaxBackward0>)gotowe api
generowanie tekstu
model = pipeline('text-generation', model='gpt2')model("Hello, I'm a computer science student", max_length=30, num_return_sequences=5)Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': "Hello, I'm a computer science student who was wondering if there's any way to generate an input on the keyboard that allows more text to be typed"},
{'generated_text': "Hello, I'm a computer science student, so I'm a lot of people to be able to participate in my own experiments and to work with those"},
{'generated_text': "Hello, I'm a computer science student studying Computer Science at University of Colorado, Boulder. My major is in Mathematics with a Bachelor of Science in Mathematics"},
{'generated_text': "Hello, I'm a computer science student, so I really enjoy reading, reading, watching video games, even browsing the internet. I enjoy interacting with"},
{'generated_text': "Hello, I'm a computer science student here at USC. Well, I've been studying computer science from college through university. It's like studying science"}]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)Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': 'I want to contribute to Google\'s Computer Vision Program, which is doing extensive work on big data applications like deep learning," Hernández-'},
{'generated_text': "I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data analytics.\n\nThere is a huge amount of data"},
{'generated_text': "I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data and machine learning, and to contribute to DARPA's"},
{'generated_text': 'I want to contribute to Google\'s Computer Vision Program, which is doing extensive work on big-picture approaches to machine-learning," Oakeshott'},
{'generated_text': "I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data and advanced machine learning. We want to build a collaborative"}]sentiment analysis
from transformers import pipeline
model = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english')model<transformers.pipelines.text_classification.TextClassificationPipeline at 0x7f171de93670>
model("I'm very happy. Today is the beatifull weather")[{'label': 'POSITIVE', 'score': 0.999847412109375}]model("It's raining. What a terrible day...")[{'label': 'NEGATIVE', 'score': 0.9946850538253784}]NER
model = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english')from transformers import pipeline
model = pipeline("ner")No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english)
text = "George Washington went to Washington"model(text)[{'entity': 'I-PER',
'score': 0.9983942,
'index': 1,
'word': 'George',
'start': 0,
'end': 6},
{'entity': 'I-PER',
'score': 0.9992505,
'index': 2,
'word': 'Washington',
'start': 7,
'end': 17},
{'entity': 'I-LOC',
'score': 0.98389727,
'index': 5,
'word': 'Washington',
'start': 26,
'end': 36}]masked language modelling
ZADANIE (10 minut)
przewidzieć
text summarization
summarizer = pipeline("summarization")No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)
ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))[{'summary_text': ' Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002 . At one time, she was married to eight men at once, prosecutors say .'}]
ZADANIE DOMOWE
- finetunować klasyfikator bazujący na jakieś pretrenowanej sieć typu transformer (np BERT, Roberta). Można użyć dowolnej biblioteki (np hugging face, fairseq)
- termin 20.06, 60 punktów