Saif10/StructBERT-encoder

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

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)