roberta-large-mnli
Maintainer: FacebookAI
135
👁️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The roberta-large-mnli model is a version of the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. This model was developed by FacebookAI and can be used for zero-shot classification tasks, including zero-shot sentence-pair classification and zero-shot sequence classification.
Similar models include the RoBERTa large model, the XLM-RoBERTa large model, and the XLM-RoBERTa large-XNLI model. These models are all based on the RoBERTa architecture and have been fine-tuned on various natural language inference tasks.
Model inputs and outputs
Inputs
- Text sequences: The model can take text sequences as input for zero-shot classification tasks.
Outputs
- Classification labels: The model outputs classification labels for the input text sequences.
Capabilities
The roberta-large-mnli model can be used for zero-shot classification tasks, where the model is able to classify text into categories without being trained on those specific categories. This can be useful for a variety of applications, such as sentiment analysis, topic classification, and intent detection.
What can I use it for?
The roberta-large-mnli model can be used for a variety of zero-shot classification tasks, such as:
- Sentiment analysis: Classifying text as positive, negative, or neutral.
- Topic classification: Classifying text into different topics or categories.
- Intent detection: Identifying the intent behind a user's text, such as a request for information or a complaint.
You can use the model with the zero-shot-classification pipeline in the Hugging Face Transformers library.
Things to try
One interesting thing to try with the roberta-large-mnli model is to experiment with using different languages for the input text and the candidate labels. Since the model was pre-trained on a multilingual dataset, it may be able to perform well on zero-shot classification tasks across multiple languages.
You could also try fine-tuning the model on your own dataset to see if it improves performance on your specific use case. The model's ability to learn from the MNLI corpus may help it generalize well to other classification tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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