---
license: mit
language:
- en
library_name: transformers
tags:
- esm
- esm2
- protein language model
- biology
- protein token classification
- secondary structure prediction
---

# ESM-2 (`esm2_t6_8M_UR50D`) for Token Classification

This is a fine-tuned version of [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) trained on the token classification task
to classify amino acids in protein sequences into one of three categories `0: other`, `1: alpha helix`, `2: beta strand`. It was trained with 
[this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) and achieves 
78.13824286786025 % accuracy. 

## Using the Model

To use, try running:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import numpy as np

# 1. Prepare the Model and Tokenizer
#  Replace with the path where your trained model is saved if you're training a new model
model_dir = "AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-secondary-structure"

model = AutoModelForTokenClassification.from_pretrained(model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir)

# Define a mapping from label IDs to their string representations
label_map = {0: "Other", 1: "Helix", 2: "Strand"}

# 2. Tokenize the New Protein Sequence
new_protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT"  # Replace with your protein sequence
tokens = tokenizer.tokenize(new_protein_sequence)
inputs = tokenizer.encode(new_protein_sequence, return_tensors="pt")

# 3. Predict with the Model
with torch.no_grad():
    outputs = model(inputs).logits
    predictions = np.argmax(outputs[0].numpy(), axis=1)

# 4. Decode the Predictions
predicted_labels = [label_map[label_id] for label_id in predictions]

# Print the tokens along with their predicted labels
for token, label in zip(tokens, predicted_labels):
    print(f"{token}: {label}")
```