Create README.md

README.md

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+---
+license: mit
+datasets:
+- QizhiPei/BioT5_finetune_dataset
+language:
+- en
+---
+## Example Usage
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+def add_prefix_to_amino_acids(protein_sequence):
+    amino_acids = list(protein_sequence)
+    prefixed_amino_acids = ['<p>' + aa for aa in amino_acids]
+    new_sequence = ''.join(prefixed_amino_acids)
+    return new_sequence
+
+tokenizer = T5Tokenizer.from_pretrained("QizhiPei/biot5-base-dti-human", model_max_length=512)
+model = T5ForConditionalGeneration.from_pretrained('QizhiPei/biot5-base-dti-human')
+
+task_definition = 'Definition: Drug target interaction prediction task (a binary classification task) for the human dataset. If the given molecule and protein can interact with each other, indicate via "Yes". Otherwise, response via "No".\n\n'
+selfies_input = '[C][/C][=C][Branch1][C][\\C][C][=Branch1][C][=O][O]'
+protein_input = 'MQALRVSQALIRSFSSTARNRFQNRVREKQKLFQEDNDIPLYLKGGIVDNILYRVTMTLCLGGTVYSLYSLGWASFPRN'
+protein_input = add_prefix_to_amino_acids(protein_input)
+task_input = f'Now complete the following example -\nInput: Molecule: <bom>{selfies_input}<eom>\nProtein: <bop>{protein_input}<eop>\nOutput: '
+
+model_input = task_definition + task_input
+input_ids = tokenizer(model_input, return_tensors="pt").input_ids
+
+generation_config = model.generation_config
+generation_config.max_length = 8
+generation_config.num_beams = 1
+
+outputs = model.generate(input_ids, generation_config=generation_config)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## References
+For more information, please refer to our paper and GitHub repository.
+
+Paper: [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://arxiv.org/abs/2310.07276)
+
+GitHub: [BioT5](https://github.com/QizhiPei/BioT5)
+
+Authors: *Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan*