roberta-base-qnli
| Property | Value |
|---|---|
| License | MIT |
| Framework | PyTorch 1.7.1 |
| Base Model | RoBERTa-base |
| Maximum Accuracy | 91.54% |
What is roberta-base-qnli?
roberta-base-qnli is a fine-tuned version of the RoBERTa base model specifically optimized for the GLUE Question-Answering Natural Language Inference (QNLI) task. This model demonstrates exceptional performance with a 91.54% accuracy on the evaluation set and a minimal loss of 0.2330.
Implementation Details
The model was trained using a carefully curated learning process with Adam optimizer, utilizing a linear learning rate scheduler with a 0.06 warmup ratio. Training was conducted over 10 epochs with batch sizes of 16 for training and 8 for evaluation.
- Learning rate: 2e-05
- Training batch size: 16
- Evaluation batch size: 8
- Optimizer: Adam (betas=0.9,0.999, epsilon=1e-08)
- Training epochs: 10.0
Core Capabilities
- High-accuracy question-answering inference
- Robust performance metrics throughout training
- Stable learning progression with minimal loss variance
- Effective handling of natural language understanding tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its impressive accuracy on the QNLI task, achieving 91.54% accuracy through careful fine-tuning of the RoBERTa architecture. The training process shows consistent improvement and stability, making it particularly reliable for question-answering tasks.
Q: What are the recommended use cases?
The model is specifically designed for question-answering natural language inference tasks. It's ideal for applications requiring high-accuracy determination of whether a given answer is appropriate for a particular question, such as automated Q&A systems or text comprehension tasks.
