Update README.md

README.md

@@ -1,6 +1,83 @@
 ---
+language:
+- en
 license: mit
+tags:
+- chemistry
+- SMILES
+- product
+datasets:
+- ORD
+metrics:
+- accuracy
 ---
-# ZINC-t5-productpredicition
 
-We finetuned [ZINC-t5](https://huggingface.co/sagawa/ZINC-t5) to predict products, and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
+# 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 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
+
+## 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. -->
+
+## How to Get Started with the Model
+
+Download files and use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5-product-prediction')
+inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
+model = T5ForConditionalGeneration.from_pretrained('sagawa/ReactionT5-product-prediction')
+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)
+output = tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace(' ', '').rstrip('.')
+output # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]'
+```
+
+## 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 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.
+
+```python
+python train.py \
+    --epochs=100 \
+    --batch_size=32 \
+    --data_path='../data/all_ord_reaction_uniq_with_attr_v3.csv' \
+    --use_reconstructed_data \
+    --pretrained_model_name_or_path='sagawa/CompoundT5'
+```
+
+### Results
+
+
+
+## Citation [optional]
+
+<!-- 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)}}