Update README.md

README.md

@@ -11,31 +11,30 @@ datasets:
 metrics:
 - accuracy
 ---
+# ⚠️This is an old version of [ReactionT5-forward-v2](https://huggingface.co/sagawa/ReactionT5-forward-v2). Prediction accuracy is worse.⚠️
 
-# 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 Details
 
-<!-- Provide a longer summary of what this model is. -->
+# Model Card for ReactionT5-forward-v1
 
+This is a ReactionT5 pre-trained to predict the products of reactions. 
 
 ### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** https://github.com/sagawatatsuya/ReactionT5
-- **Paper:** {{ paper | default("[More Information Needed]", true)}}
-- **Demo:** https://huggingface.co/spaces/sagawa/predictproduct-t5
+- **Paper:** https://arxiv.org/abs/2311.06708
 
 ## 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
 
-Download files and use the code below to get started with the model.
+Use the code below to get started with the model.
 
 ```python
 from transformers import AutoTokenizer, T5ForConditionalGeneration
@@ -54,7 +53,7 @@ output # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]'
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 We used Open Reaction Database (ORD) dataset for model training.
-Following is the command used for training. For more information, please refer to the paper and GitHub repository.
+The command used for training is the following. For more information, please refer to the paper and GitHub repository.
 
 ```python
 python train.py \
@@ -79,15 +78,17 @@ python train.py \
 
 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. 
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-
-## Model Card Authors [optional]
-
-{{ model_card_authors | default("[More Information Needed]", true)}}
-
-## Model Card Contact
-
-{{ model_card_contact | default("[More Information Needed]", true)}}
+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}  
+}
+```