Zero-Shot Classification
Transformers
Safetensors
English
modernbert
text-classification
instruct
natural-language-inference
nli
mnli
Instructions to use tasksource/ModernBERT-base-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/ModernBERT-base-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tasksource/ModernBERT-base-nli") model = AutoModelForSequenceClassification.from_pretrained("tasksource/ModernBERT-base-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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# Model Card for Model ID
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ModernBERT fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI...)
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Test accuracy at 10k training steps (current version, 100k steps incoming at the end of the week).
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| test_name | test_accuracy |
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# Model Card for Model ID
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ModernBERT fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI...).
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Test accuracy at 10k training steps (current version, 100k steps incoming at the end of the week).
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| test_name | test_accuracy |
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