distilbart-mnli-12-1
Maintainer: valhalla
50
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| 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
distilbart-mnli-12-1 is the distilled version of the bart-large-mnli model, created using the "No Teacher Distillation" technique proposed by Hugging Face. This model has 12 encoder layers and 1 decoder layer, making it smaller and faster than the original bart-large-mnli model.
Compared to the baseline bart-large-mnli model, distilbart-mnli-12-1 has 87.08% matched accuracy and 87.5% mismatched accuracy, a slight performance drop from the original. However, the distilled model is significantly more efficient, being 2x smaller and faster. Additional distilled versions such as distilbart-mnli-12-3, distilbart-mnli-12-6, and distilbart-mnli-12-9 offer a range of performance and efficiency trade-offs.
Model inputs and outputs
Inputs
- Text: The model takes text as input, either as a single sequence or as a pair of sequences (e.g. premise and hypothesis for natural language inference).
Outputs
- Text classification label: The model outputs a classification label, such as "entailment", "contradiction", or "neutral" for natural language inference tasks.
- Classification probability: The model also outputs the probability of each possible classification label.
Capabilities
The distilbart-mnli-12-1 model is capable of natural language inference - determining whether one piece of text (the premise) entails, contradicts, or is neutral with respect to another piece of text (the hypothesis). This can be useful for applications like textual entailment, question answering, and language understanding.
What can I use it for?
You can use distilbart-mnli-12-1 for zero-shot text classification by posing the text to be classified as the premise and constructing hypotheses from the candidate labels. The probabilities for entailment and contradiction can then be converted to label probabilities. This approach has been shown to be effective, especially when using larger pre-trained models like BART.
The distilled model can also be fine-tuned on downstream tasks that require natural language inference, such as question answering or natural language inference datasets. The smaller size and faster inference time of distilbart-mnli-12-1 compared to the original bart-large-mnli model makes it a more efficient choice for deployment.
Things to try
One interesting thing to try is to experiment with the different distilled versions of the bart-large-mnli model, such as distilbart-mnli-12-3, distilbart-mnli-12-6, and distilbart-mnli-12-9. These offer a range of performance and efficiency trade-offs that you can evaluate for your specific use case. Additionally, you can explore using the model for zero-shot text classification on a variety of datasets and tasks to see how it performs.
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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