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
+
+tokenizer = T5Tokenizer.from_pretrained("QizhiPei/biot5-base-text2mol", model_max_length=512)
+model = T5ForConditionalGeneration.from_pretrained('QizhiPei/biot5-base-text2mol')
+
+task_definition = 'Definition: You are given a molecule description in English. Your job is to generate the molecule SELFIES that fits the description.\n\n'
+text_input = 'The molecule is a monocarboxylic acid anion obtained by deprotonation of the carboxy and sulfino groups of 3-sulfinopropionic acid. Major microspecies at pH 7.3 It is an organosulfinate oxoanion and a monocarboxylic acid anion. It is a conjugate base of a 3-sulfinopropionic acid.'
+task_input = f'Now complete the following example -\nInput: {text_input}\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 = 512
+generation_config.num_beams = 1
+
+outputs = model.generate(input_ids, generation_config=generation_config)
+output_selfies = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(' ', '')
+print(output_selfies)
+
+import selfies as sf
+output_smiles = sf.decoder(output_selfies)
+print(output_smiles)
+```
+
+## 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*