---
language: en
tags:
- bert
- masked-language-model
- structbert
- dsa
---

# StructBERT Encoder

This model is a **StructBERT variant** fine-tuned on a custom Data Structures and Algorithms (DSA) corpus.  

## Model Details

- **Architecture:** BERT (Masked Language Modeling)
- **Tokenizer:** BERT tokenizer
- **Training Data:** Merged DSA corpus (~32k lines)
- **Framework:** Hugging Face Transformers

## Intended Use

- Predict missing tokens in DSA-related text
- Research, education, and NLP experimentation

## Limitations

- Small corpus (~32k lines), so may not generalize beyond DSA content
- Token predictions may be biased toward training examples
- Not intended for production-grade applications

## Example Usage

```python
from transformers import BertTokenizer, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained("Saif10/StructBERT-encoder")
model = BertForMaskedLM.from_pretrained("Saif10/StructBERT-encoder")

text = "Binary search works by dividing the [MASK] into two halves."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_token_id = outputs.logits.argmax(-1)
predicted_token = tokenizer.decode(predicted_token_id[0])
print(predicted_token)