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--- |
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language: |
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- en |
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tags: |
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- token-classification |
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- text-classification |
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- question-answering |
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- text2text-generation |
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- text-generation |
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datasets: |
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- pubmed |
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--- |
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# SciFive Pubmed Base |
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## Introduction |
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Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598) |
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Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_ |
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## How to use |
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For more details, do check out [our Github repo](https://github.com/justinphan3110/SciFive). |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-Pubmed") |
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model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-Pubmed") |
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sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ." |
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text = sentence + " </s>" |
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encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt") |
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") |
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outputs = model.generate( |
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input_ids=input_ids, attention_mask=attention_masks, |
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max_length=256, |
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early_stopping=True |
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) |
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for output in outputs: |
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line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) |
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print(line) |
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``` |
