metadata
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
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)