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README.md

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+# ProtBERT-Unmasking
+
+This model is a fine-tuned version of ProtBERT specifically optimized for unmasking protein sequences. It can predict masked amino acids in protein sequences based on the surrounding context.
+
+## Model Description
+
+- **Base Model**: ProtBERT
+- **Task**: Protein Sequence Unmasking
+- **Training**: Fine-tuned on masked protein sequences
+- **Use Case**: Predicting missing or masked amino acids in protein sequences
+
+## Usage
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForMaskedLM.from_pretrained("faceless-void/protbert-sequence-unmasking")
+tokenizer = AutoTokenizer.from_pretrained("faceless-void/protbert-sequence-unmasking")
+
+# Example usage
+sequence = "MALN[MASK]KFGP[MASK]LVRK"
+inputs = tokenizer(sequence, return_tensors="pt")
+outputs = model(**inputs)
+predictions = outputs.logits
+```
+
+## Limitations and Biases
+
+This model is specifically designed for protein sequence unmasking and may not perform optimally for other protein-related tasks.
+
+## Training Details
+
+The model was fine-tuned from the original ProtBERT model with specific focus on masked sequence prediction.