Create README.md

@misc
{StructBERT2025,
author = {Saif},
title = {StructBERT Encoder for DSA},
year = {2025},
howpublished = {\url{https://huggingface.co/Saif10/StructBERT-encoder}}
}

README.md

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+---
+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)