* Created using Colaboratory * [examples] reorganize files * remove run_tpu_glue.py as superseded by TPU support in Trainer * Bugfix: int, not tuple * move files around
51 lines
1.9 KiB
Markdown
51 lines
1.9 KiB
Markdown
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# Roberta Large STS-B
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This model is a fine tuned RoBERTA model over STS-B.
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It was trained with these params:
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!python /content/transformers/examples/text-classification/run_glue.py \
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--model_type roberta \
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--model_name_or_path roberta-large \
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--task_name STS-B \
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--do_train \
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--do_eval \
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--do_lower_case \
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--data_dir /content/glue_data/STS-B/ \
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--max_seq_length 128 \
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--per_gpu_eval_batch_size=8 \
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--per_gpu_train_batch_size=8 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir /content/roberta-sts-b
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## How to run
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```python
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import toolz
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import torch
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batch_size = 6
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def roberta_similarity_batches(to_predict):
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batches = toolz.partition(batch_size, to_predict)
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similarity_scores = []
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for batch in batches:
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sentences = [(sentence_similarity["sent1"], sentence_similarity["sent2"]) for sentence_similarity in batch]
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batch_scores = similarity_roberta(model, tokenizer,sentences)
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similarity_scores = similarity_scores + batch_scores[0].cpu().squeeze(axis=1).tolist()
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return similarity_scores
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def similarity_roberta(model, tokenizer, sent_pairs):
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batch_token = tokenizer.batch_encode_plus(sent_pairs, pad_to_max_length=True, max_length=500)
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res = model(torch.tensor(batch_token['input_ids']).cuda(), attention_mask=torch.tensor(batch_token["attention_mask"]).cuda())
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return res
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similarity_roberta(model, tokenizer, [('NEW YORK--(BUSINESS WIRE)--Rosen Law Firm, a global investor rights law firm, announces it is investigating potential securities claims on behalf of shareholders of Vale S.A. ( VALE ) resulting from allegations that Vale may have issued materially misleading business information to the investing public',
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'EQUITY ALERT: Rosen Law Firm Announces Investigation of Securities Claims Against Vale S.A. – VALE')])
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```
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