Update README.md

README.md

@@ -15,9 +15,9 @@ widget:
 - text: CCO
   example_title: ethanol
 ---
-# Model Card for Model ID
+# SMILES2IUPAC-canonical-small
 
-SMILES2IUPAC-small was designed to accurately translate SMILES chemical names to IUPAC standards. 
+SMILES2IUPAC-canonical-small was designed to accurately translate SMILES chemical names to IUPAC standards. 
 
 ## Model Details
 
@@ -34,8 +34,8 @@ SMILES2IUPAC-small is based on the MT5 model with optimizations in implementing
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** coming soon
-- **Paper [optional]:** coming soon
-- **Demo [optional]:** huggingface.co/spaces/knowledgator/ChemicalConverters
+- **Paper:** coming soon
+- **Demo:** [ChemicalConverters](https://huggingface.co/spaces/knowledgator/ChemicalConverters)
 
 ## Quickstart
 Firstly, install the library:
@@ -56,7 +56,8 @@ your SMILES sequence.
 #### To perform simple translation, follow the example:
 ```python
 from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="smiles2iupac-canonical-small")
+
+converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small")
 print(converter.smiles_to_iupac('CCO'))
 print(converter.smiles_to_iupac(['<SYST>CCO', '<TRAD>CCO', '<BASE>CCO']))
 ```
@@ -67,7 +68,8 @@ print(converter.smiles_to_iupac(['<SYST>CCO', '<TRAD>CCO', '<BASE>CCO']))
 #### Processing in batches:
 ```python
 from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="smiles2iupac-canonical-small")
+
+converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small")
 print(converter.smiles_to_iupac(["<BASE>C=CC=C" for _ in range(10)], num_beams=1, 
                                 process_in_batch=True, batch_size=1000))
 ```
@@ -79,7 +81,8 @@ It's possible to validate the translations by reverse translation into IUPAC
 and calculating Tanimoto similarity of two molecules fingerprints.
 ````python
 from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="smiles2iupac-canonical-small")
+
+converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small")
 print(converter.smiles_to_iupac('CCO', validate=True))
 ````
 ````text
@@ -90,7 +93,8 @@ The larger is Tanimoto similarity, the larger is probability, that the predictio
 You can also process validation manually:
 ```python
 from chemicalconverters import NamesConverter
-validation_model = NamesConverter(model_name="iupac2smiles-canonical-base")
+
+validation_model = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
 print(NamesConverter.validate_iupac(input_sequence='CCO', predicted_sequence='CCO', validation_model=validation_model))
 ```
 ```text
@@ -117,13 +121,13 @@ The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.0003, bat
 | STOUT V2.0 (according to our tests) |         | 0.89                 | 128      |
 *According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4
 
-## Citation [optional]
+## Citation
 Coming soon.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+@BioMike
 
 ## Model Card Contact
 
-[More Information Needed]
+info@knowledgator.com