Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v1
deberta-mnli
Instructions to use NDugar/v3-Large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/v3-Large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/v3-Large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/v3-Large-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/v3-Large-mnli") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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README.md
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language: en
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- deberta-v1
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- deberta-mnli
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tasks: mnli
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thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
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license: mit
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pipeline_tag: zero-shot-classification
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---
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI dataset.
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language: en
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license: mit
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tags:
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- deberta-v1
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- deberta-mnli
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tasks: mnli
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thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
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pipeline_tag: zero-shot-classification
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base_model: microsoft/deberta-v3-large
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI dataset.
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