37 KiB
37 KiB
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 = ['/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia9_ngramowy_model_jDDezykowy/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 transformersimport torchfrom transformers import pipeline, set_seedfrom transformers import RobertaTokenizer, RobertaModeltokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')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, -7.2129e-03, ..., -4.6295e-02,
-3.9316e-02, 4.5264e-04],
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8.4371e-02, 1.4826e-01],
[-2.3786e-02, -1.2086e-02, 7.8233e-02, ..., -4.9132e-01,
1.2500e-01, 3.3293e-01],
...,
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1.9186e-02, 1.5355e-01],
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7.3958e-02, 3.7870e-02],
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-3.5529e-02, -2.6777e-02]]], grad_fn=<NativeLayerNormBackward>), pooler_output=tensor([[ 5.1994e-03, -2.1290e-01, -2.2585e-01, -9.3315e-02, 1.1761e-01,
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-1.2860e-01, -2.5987e-02, -1.1620e-01]], grad_fn=<TanhBackward>), 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=<NativeLayerNormBackward>)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, 8.7264e-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, 7.9328e-06, ..., 1.8371e-02,
5.9146e-02, 7.1377e-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, 4.1909e-03, 9.0263e-03, ..., 2.1102e-06,
5.4437e-07, 6.7581e-06],
[8.3596e-01, 6.3265e-02, 7.9091e-02, ..., 1.4975e-05,
2.1750e-06, 2.3804e-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,
8.0127e-05, 2.8887e-03],
[8.9282e-01, 2.4450e-04, 5.3892e-03, ..., 8.5178e-04,
9.8922e-05, 2.7169e-03],
...,
[9.3745e-01, 2.0096e-06, 4.1223e-06, ..., 4.7319e-02,
3.8060e-03, 6.3264e-03],
[9.5799e-01, 1.2817e-04, 1.0723e-05, ..., 1.0232e-03,
2.1168e-02, 3.7038e-03],
[9.1897e-01, 4.5952e-04, 7.4514e-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,
8.6231e-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, 2.1891e-01],
[9.4142e-01, 3.3256e-05, 6.0546e-06, ..., 9.1890e-04,
8.7666e-03, 3.8735e-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,
6.4891e-05, 2.8477e-03],
...,
[2.1335e-01, 9.7233e-06, 6.9469e-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=<SoftmaxBackward>)gotowe api
generowanie tekstu
model = pipeline('text-generation', model='gpt2')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'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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 by trade. I don\'t really like science."\n\nThen I hear him say: "I love the'},
{'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"},
{'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"},
{'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."},
{'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"}]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. It is also working on creating a new type of"},
{'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"},
{'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"},
{'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"},
{'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"}]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 0x7f8e45b319d0>
model("I'm very happy. Today is the beatifull weather")[{'label': 'POSITIVE', 'score': 0.9998474717140198}]model("It's raining. What a terrible day...")[{'label': 'NEGATIVE', 'score': 0.9946851134300232}]NER
model = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english')from transformers import pipeline
model = pipeline("ner")text = "George Washington went to Washington"model(text)[{'word': 'George',
'score': 0.9983943104743958,
'entity': 'I-PER',
'index': 1,
'start': 0,
'end': 6},
{'word': 'Washington',
'score': 0.9992505311965942,
'entity': 'I-PER',
'index': 2,
'start': 7,
'end': 17},
{'word': 'Washington',
'score': 0.98389732837677,
'entity': 'I-LOC',
'index': 5,
'start': 26,
'end': 36}]masked language modelling
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