Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+tags:
+- CodonTransformer
+- Computational Biology
+- Machine Learning
+- Bioinformatics
+- Synthetic Biology
+license: apache-2.0
+pipeline_tag: token-classification
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c9888b3137cc529d0761c4/GqKutRwiGGif69Gjd8Df3.png)
+
+**CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort.
+Note this is the pretrained model. We recommend using the finetuned model available at https://huggingface.co/adibvafa/CodonTransformer
+
+## Use Case
+**For an interactive demo, check out our [Google Colab Notebook.](https://adibvafa.github.io/CodonTransformer/GoogleColab)**
+<br></br>
+After installing CodonTransformer, you can use:
+```python
+import torch
+from transformers import AutoTokenizer, BigBirdForMaskedLM
+from CodonTransformer.CodonPrediction import predict_dna_sequence
+from CodonTransformer.CodonJupyter import format_model_output
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("adibvafa/CodonTransformer")
+model = BigBirdForMaskedLM.from_pretrained("adibvafa/CodonTransformer").to(DEVICE)
+
+
+# Set your input data
+protein = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG"
+organism = "Escherichia coli general"
+
+
+# Predict with CodonTransformer
+output = predict_dna_sequence(
+    protein=protein,
+    organism=organism,
+    device=DEVICE,
+    tokenizer_object=tokenizer,
+    model_object=model,
+    attention_type="original_full",
+)
+print(format_model_output(output))
+```
+The output is:
+<br>
+
+
+```python
+-----------------------------
+|          Organism         |
+-----------------------------
+Escherichia coli general
+
+-----------------------------
+|       Input Protein       |
+-----------------------------
+MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG
+
+-----------------------------
+|      Processed Input      |
+-----------------------------
+M_UNK A_UNK L_UNK W_UNK M_UNK R_UNK L_UNK L_UNK P_UNK L_UNK L_UNK A_UNK L_UNK L_UNK A_UNK L_UNK W_UNK G_UNK P_UNK D_UNK P_UNK A_UNK A_UNK A_UNK F_UNK V_UNK N_UNK Q_UNK H_UNK L_UNK C_UNK G_UNK S_UNK H_UNK L_UNK V_UNK E_UNK A_UNK L_UNK Y_UNK L_UNK V_UNK C_UNK G_UNK E_UNK R_UNK G_UNK F_UNK F_UNK Y_UNK T_UNK P_UNK K_UNK T_UNK R_UNK R_UNK E_UNK A_UNK E_UNK D_UNK L_UNK Q_UNK V_UNK G_UNK Q_UNK V_UNK E_UNK L_UNK G_UNK G_UNK __UNK
+
+-----------------------------
+|       Predicted DNA       |
+-----------------------------
+ATGGCTTTATGGATGCGTCTGCTGCCGCTGCTGGCGCTGCTGGCGCTGTGGGGCCCGGACCCGGCGGCGGCGTTTGTGAATCAGCACCTGTGCGGCAGCCACCTGGTGGAAGCGCTGTATCTGGTGTGCGGTGAGCGCGGCTTCTTCTACACGCCCAAAACCCGCCGCGAAGCGGAAGATCTGCAGGTGGGCCAGGTGGAGCTGGGCGGCTAA
+```
+
+
+## Additional Resources
+- **Project Website** <br>
+  https://adibvafa.github.io/CodonTransformer/
+
+- **GitHub Repository** <br>
+  https://github.com/Adibvafa/CodonTransformer
+
+- **Google Colab Demo** <br>
+  https://adibvafa.github.io/CodonTransformer/GoogleColab
+
+- **PyPI Package** <br>
+  https://pypi.org/project/CodonTransformer/
+
+- **Paper** <br>
+  TBD