Bart Large Mnli
This model uses the BART-large architecture, trained on the MultiNLI dataset, and can be used for zero-shot text classification tasks.
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Release Time : 3/2/2022
Model Overview
A natural language inference (NLI) zero-shot text classifier based on the BART large model, capable of predicting whether a topic label is suitable for a given text sequence, even if the label was not seen during training.
Model Features
Zero-shot learning capability
Classify new labels without training on specific labels
Multi-label classification
Supports assigning multiple relevant labels to text simultaneously
NLI-based framework
Utilizes natural language inference paradigm for text classification
Model Capabilities
Zero-shot text classification
Multi-label classification
Natural language inference
Use Cases
Text classification
Topic classification
Classify text with unseen topic labels
Accurately identifies text topics
Sentiment analysis
Analyze text sentiment tendencies using custom sentiment labels
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