| --- |
| 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) |
| |
