| --- |
| library_name: transformers |
| license: apache-2.0 |
| datasets: |
| - nohurry/Opus-4.6-Reasoning-3000x-filtered |
| language: |
| - ab |
| base_model: |
| - Qwen/Qwen3.5-35B-A3B |
| new_version: Qwen/Qwen3.5-35B-A3B |
| pipeline_tag: text-classification |
| tags: |
| - code |
| --- |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - glue |
| - mrpc |
| datasets: |
| - glue |
| language: |
| - en |
| --- |
|
|
| # bert-finetuned-mrpc-v2 |
|
|
| Fine-tuned BERT-base-uncased on MRPC (GLUE benchmark) for paraphrase detection. |
|
|
| ## Model details |
|
|
| - Base model: google-bert/bert-base-uncased |
| - Task: Binary classification (paraphrase or not) |
| - Language: English |
| - Training data: GLUE MRPC train split |
| - Evaluation data: GLUE MRPC validation split |
| - Epochs: 3 |
| - Batch size: 16 |
| - Learning rate: 2e-5 |
|
|
| ## How to use |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="tu-usuario/bert-finetuned-mrpc-v2") |
| result = classifier("The two sentences mean the same thing.") |
| print(result) |
