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--- |
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inference: |
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parameters: |
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do_sample: True |
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max_length: 500 |
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top_p: 0.9 |
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top_k: 20 |
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temperature: 1 |
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num_return_sequences: 10 |
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widget: |
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- text: "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)." |
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example_title: "BERT abstract" |
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--- |
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|
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large") |
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tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large") |
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text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task. |
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input_ids = tokenizer.encode(text, return_tensors='pt') |
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outputs = model.generate( |
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input_ids, |
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do_sample=True, |
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max_length=500, |
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top_p=0.9, |
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top_k=20, |
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temperature=1, |
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num_return_sequences=10, |
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) |
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|
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print("Output:\n" + 100 * '-') |
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for i, output in enumerate(outputs): |
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print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True))) |
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|
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``` |
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GitHub: https://github.com/ArvinZhuang/BiTAG |
