Instructions to use potsawee/deberta-v3-large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use potsawee/deberta-v3-large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="potsawee/deberta-v3-large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("potsawee/deberta-v3-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("potsawee/deberta-v3-large-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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# DeBERTa-v3 (large) fine-tuned to Multi-NLI (MNLI)
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This model is for Textual Entailment (aka NLI), i.e., predict whether `textA` is supported by `textB`. More specifically, it's a 2-way classification where the relationship between `textA` and `textB`
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- Input: (`textA`, `textB`)
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- Output: prob(entail), prob(contradict)
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# DeBERTa-v3 (large) fine-tuned to Multi-NLI (MNLI)
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This model is for Textual Entailment (aka NLI), i.e., predict whether `textA` is supported by `textB`. More specifically, it's a 2-way classification where the relationship between `textA` and `textB` can be **entail, neutral, contradict**.
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- Input: (`textA`, `textB`)
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- Output: prob(entail), prob(contradict)
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