Instructions to use razent/SciFive-large-Pubmed_PMC-MedNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-large-Pubmed_PMC-MedNLI with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-Pubmed_PMC-MedNLI") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC-MedNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
root commited on
Commit ·
10e8e91
1
Parent(s): 2dc3ee3
Add SciFive MedNLI models
Browse files- README.md +48 -0
- config.json +38 -0
- pytorch_model.bin +3 -0
- spiece.model +3 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
README.md
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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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- pmc/open_access
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---
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# SciFive Pubmed+PMC Large
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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-large-Pubmed_PMC")
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model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC")
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sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ."
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text = "ncbi_ner: " + 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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```
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config.json
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{
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 4096,
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"d_kv": 64,
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"d_model": 1024,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"gradient_checkpointing": false,
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 1024,
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"num_decoder_layers": 24,
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"num_heads": 16,
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"num_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"nli": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 256,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "mednli: "
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.17.0",
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"use_cache": true,
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"vocab_size": 32128
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:9a348b5c18206449576ce89e97932fa62777db440dd1194407e90fe85ca78eae
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size 2950897543
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
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size 791656
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:68ed08a95f635e6d2de102d21db1b8c831782659dd8ceec55c655044ae302780
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size 2951711704
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tokenizer.json
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