Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-small
deberta-v3
deberta
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Instructions to use tasksource/deberta-small-long-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/deberta-small-long-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="tasksource/deberta-small-long-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tasksource/deberta-small-long-nli") model = AutoModelForSequenceClassification.from_pretrained("tasksource/deberta-small-long-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
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# Model Card for DeBERTa-v3-small-tasksource-nli
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[DeBERTa-v3-small](https://hf.co/microsoft/deberta-v3-small) with context length of 1680 fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
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Training data include
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This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning.
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# Model Card for DeBERTa-v3-small-tasksource-nli
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[DeBERTa-v3-small](https://hf.co/microsoft/deberta-v3-small) with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
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Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks.
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This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning.
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