Instructions to use tasksource/deberta-base-long-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/deberta-base-long-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="tasksource/deberta-base-long-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tasksource/deberta-base-long-nli") model = AutoModelForSequenceClassification.from_pretrained("tasksource/deberta-base-long-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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# Model Card for Model ID
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Upcoming: longer training + 1280 tokens context length.
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# Model Card for Model ID
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deberta-v3-base with context length of 1280 fine-tuned on tasksource for 150k steps. I oversampled tasks like 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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