deberta-v3-large-mnli
| Property | Value |
|---|---|
| Base Model | DeBERTa-v3-large |
| Training Dataset | MNLI |
| Author | potsawee |
| Paper | SelfCheckGPT Paper |
What is deberta-v3-large-mnli?
This is a specialized version of DeBERTa-v3-large fine-tuned for textual entailment tasks using the Multi-NLI (MNLI) dataset. The model performs binary classification to determine whether one text (hypothesis) is supported by or contradicts another text (premise). While trained on three labels (entail, neutral, contradict), the final model focuses on entailment and contradiction predictions only.
Implementation Details
The model was trained for 3 epochs with a batch size of 16, using hypothesis as textA and premise as textB. It utilizes the powerful DeBERTa-v3-large architecture and outputs probability scores for entailment and contradiction relationships between input text pairs.
- Built on DeBERTa-v3-large architecture
- Fine-tuned on MNLI dataset
- Optimized for binary classification (entail/contradict)
- Processes paired text inputs for relationship analysis
Core Capabilities
- Textual entailment assessment
- Binary classification of text relationships
- Probability distribution output for entailment and contradiction
- Efficient processing of paired text inputs
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on binary textual entailment, removing the neutral classification to provide clearer entailment vs. contradiction predictions. It leverages the powerful DeBERTa-v3-large architecture while being optimized for specific relationship analysis tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring analysis of text relationships, such as fact-checking, content verification, and logical relationship assessment between statements. It's particularly useful in scenarios where determining whether one statement supports or contradicts another is crucial.
