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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- chemistry |
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- SMILES |
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- product |
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datasets: |
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- ORD |
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metrics: |
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- accuracy |
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--- |
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# ⚠️This is an old version of [ReactionT5v2-forward](https://huggingface.co/sagawa/ReactionT5v2-forward). Prediction accuracy is worse.⚠️ |
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# Model Card for ReactionT5v1-forward |
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This is a ReactionT5 pre-trained to predict the products of reactions. |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/sagawatatsuya/ReactionT5 |
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- **Paper:** https://arxiv.org/abs/2311.06708 |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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You can use this model for forward reaction prediction or fine-tune this model with your dataset. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, T5ForConditionalGeneration |
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tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5-product-prediction') |
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inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt') |
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model = T5ForConditionalGeneration.from_pretrained('sagawa/ReactionT5-product-prediction') |
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output = model.generate(**inp, min_length=6, max_length=109, num_beams=1, num_return_sequences=1, return_dict_in_generate=True, output_scores=True) |
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output = tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace(' ', '').rstrip('.') |
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output # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]' |
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``` |
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## Training Details |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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We used Open Reaction Database (ORD) dataset for model training. |
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The command used for training is the following. For more information, please refer to the paper and GitHub repository. |
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```python |
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python train.py \ |
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--epochs=100 \ |
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--batch_size=32 \ |
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--data_path='../data/all_ord_reaction_uniq_with_attr_v3.csv' \ |
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--use_reconstructed_data \ |
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--pretrained_model_name_or_path='sagawa/CompoundT5' |
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``` |
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### Results |
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| Model | Training set | Test set | Top-1 [% acc.] | Top-2 [% acc.] | Top-3 [% acc.] | Top-5 [% acc.] | |
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|----------------------|---------------------------|----------|----------------|----------------|----------------|----------------| |
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| Sequence-to-sequence | USPTO | USPTO | 80.3 | 84.7 | 86.2 | 87.5 | |
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| WLDN | USPTO | USPTO | 80.6 (85.6) | 90.5 | 92.8 | 93.4 | |
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| Molecular Transformer| USPTO | USPTO | 88.8 | 92.6 | – | 94.4 | |
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| T5Chem | USPTO | USPTO | 90.4 | 94.2 | – | 96.4 | |
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| CompoundT5 | USPTO | USPTO | 88.0 | 92.4 | 93.9 | 95.0 | |
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| ReactionT5 | - | USPTO | 0.0 <85.0> | 0.0 <90.6> | 0.0 <92.3> | 0.0 <93.8> | |
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Performance comparison of Compound T5, ReactionT5, and other models in product prediction. The values enclosed in ‘<>’ in the table represent the scores of the model that was fine-tuned on 200 reactions from the USPTO dataset. The score enclosed in ‘()’ is the one reported in the original paper. |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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arxiv link: https://arxiv.org/abs/2311.06708 |
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``` |
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@misc{sagawa2023reactiont5, |
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title={ReactionT5: a large-scale pre-trained model towards application of limited reaction data}, |
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author={Tatsuya Sagawa and Ryosuke Kojima}, |
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year={2023}, |
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eprint={2311.06708}, |
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archivePrefix={arXiv}, |
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primaryClass={physics.chem-ph} |
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} |
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``` |
