Update README.md

README.md

@@ -1,3 +1,106 @@
----
-license: mit
----
+---
+language:
+- en
+license: mit
+tags:
+- chemistry
+- SMILES
+- product
+datasets:
+- ORD
+metrics:
+- accuracy
+---
+
+# Model Card for ReactionT5-product-prediction
+
+This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
+
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/sagawatatsuya/ReactionT5v2
+- **Paper:** https://arxiv.org/abs/2311.06708
+- **Demo:** https://huggingface.co/spaces/sagawa/predictproduct-t5
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+You can use this model for forward reaction prediction or fine-tune this model with your dataset.
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("sagawa/ReactionT5-forward-v2", return_tensors="pt")
+model = AutoModelForSeq2SeqLM.from_pretrained("sagawa/ReactionT5-forward-v2")
+
+inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
+output = model.generate(**inp, num_beams=1, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
+output = tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace(' ', '').rstrip('.')
+output # 'CN1CCC=C(CO)C1'
+```
+
+## Training Details
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+We used the Open Reaction Database (ORD) dataset for model training.
+The command used for training is the following. For more information, please refer to the paper and GitHub repository.
+
+```python
+python train_without_duplicates.py \
+    --model='t5' \
+    --epochs=100 \
+    --lr=1e-3 \
+    --batch_size=32 \
+    --input_max_len=150 \
+    --target_max_len=100 \
+    --weight_decay=0.01 \
+    --evaluation_strategy='epoch' \
+    --save_strategy='epoch' \
+    --logging_strategy='epoch' \
+    --train_data_path='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_train.csv' \
+    --valid_data_path='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_valid.csv' \
+    --test_data_path='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_test.csv' \
+    --USPTO_test_data_path='/home/acf15718oa/ReactionT5_neword/data/USPTO_MIT/MIT_separated/test.csv' \
+    --disable_tqdm \
+    --pretrained_model_name_or_path='sagawa/ZINC-t5'
+```
+
+### Results
+
+
+| Model                | Training set              | Test set | Top-1 [% acc.] | Top-2 [% acc.] | Top-3 [% acc.] | Top-5 [% acc.] |
+|----------------------|---------------------------|----------|----------------|----------------|----------------|----------------|
+| Sequence-to-sequence | USPTO_MIT                     | USPTO_MIT    | 80.3           | 84.7           | 86.2           | 87.5           |
+| WLDN                 | USPTO_MIT                     | USPTO_MIT    | 80.6 (85.6)    | 90.5           | 92.8           | 93.4           |
+| Molecular Transformer| USPTO_MIT                     | USPTO_MIT    | 88.8           | 92.6           | –              | 94.4           |
+| T5Chem               | USPTO_MIT                     | USPTO_MIT    | 90.4           | 94.2           | –              | 96.4           |
+| CompoundT5           | USPTO_MIT                     | USPTO_MIT    | 86.6           | 89.5           | 90.4           | 91.2           |
+| ReactionT5 (This model)          | ORD                       | USPTO_MIT    | 92.8     | 95.6     | 96.4     | 97.1     |
+| ReactionT5           | USPTO_MIT                       | USPTO_MIT    | 97.5     | 98.6     | 98.8     | 99.0     |
+
+Performance comparison of Compound T5, ReactionT5, and other models in product prediction.
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+arxiv link: https://arxiv.org/abs/2311.06708
+```
+@misc{sagawa2023reactiont5,  
+      title={ReactionT5: a large-scale pre-trained model towards application of limited reaction data}, 
+      author={Tatsuya Sagawa and Ryosuke Kojima},  
+      year={2023},  
+      eprint={2311.06708},  
+      archivePrefix={arXiv},  
+      primaryClass={physics.chem-ph}  
+}
+```