Update README.md

README.md

@@ -43,8 +43,6 @@ Firstly, install the library:
 pip install chemical-converters
 ```
 ### SMILES to IUPAC
-You can choose pretrained model from table in the section "Models", 
-but we recommend to use model "smiles2iupac-canonical-base".
 #### ! Preferred IUPAC style
 To choose the preferred IUPAC style, place style tokens before 
 your SMILES sequence.
@@ -58,7 +56,7 @@ your SMILES sequence.
 #### To perform simple translation, follow the example:
 ```python
 from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="smiles2iupac-canonical-base")
+converter = NamesConverter(model_name="smiles2iupac-canonical-small")
 print(converter.smiles_to_iupac('CCO'))
 print(converter.smiles_to_iupac(['<SYST>CCO', '<TRAD>CCO', '<BASE>CCO']))
 ```
@@ -69,7 +67,7 @@ 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-base")
+converter = NamesConverter(model_name="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))
 ```
@@ -81,13 +79,13 @@ 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-base")
+converter = NamesConverter(model_name="smiles2iupac-canonical-small")
 print(converter.smiles_to_iupac('CCO', validate=True))
 ````
 ````text
 ['ethanol'] 1.0
 ````
-The larger is Tanimoto similarity, the more is probability, that the prediction was correct.
+The larger is Tanimoto similarity, the larger is probability, that the prediction was correct.
 
 You can also process validation manually:
 ```python
@@ -98,85 +96,16 @@ print(NamesConverter.validate_iupac(input_sequence='CCO', predicted_sequence='CC
 ```text
 1.0
 ```
-!Note validation was not implemented in processing in batches.
-
-### IUPAC to SMILES
-You can choose pretrained model from table in the section "Models", 
-but we recommend to use model "iupac2smiles-canonical-base".
-#### To perform simple translation, follow the example:
-```python
-from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="iupac2smiles-canonical-base")
-print(converter.smiles_to_iupac('ethanol'))
-print(converter.smiles_to_iupac(['ethanol', 'ethanol', 'ethanol']))
-```
-```text
-['CCO']
-['CCO', 'CCO', 'CCO']
-```
-#### Processing in batches:
-```python
-from chemicalconverters import NamesConverter
-converter = NamesConverter(model_name="smiles2iupac-canonical-base")
-print(converter.smiles_to_iupac(["buta-1,3-diene" for _ in range(10)], num_beams=1, 
-                                process_in_batch=True, batch_size=1000))
-```
-```text
-['<SYST>C=CC=C', '<SYST>C=CC=C'...]
-```
-Our models also predict IUPAC styles from the table:
-
-| Style Token | Description                                                                                        |
-|-------------|----------------------------------------------------------------------------------------------------|
-| `<BASE>`    | The most known name of the substance, sometimes is the mixture of traditional and systematic style |
-| `<SYST>`    | The totally systematic style without trivial names                                                 |
-| `<TRAD>`    | The style is based on trivial names of the parts of substances                                     |
-
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.0003, batch_size=1024 for 2 epochs.
 
 ## Evaluation
 
@@ -209,43 +138,12 @@ Use the code below to get started with the model.
 #### Summary
 
 
-
 ## Model Examination [optional]
 
 <!-- Relevant interpretability work for the model goes here -->
 
 [More Information Needed]
 
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
 ## 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. -->