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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tasksource/ModernBERT-base-nli") model = AutoModelForSequenceClassification.from_pretrained("tasksource/ModernBERT-base-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
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| test_name | test_accuracy |
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| glue/mnli | 0.87 |
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It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
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The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top. Further gains can be obtained by fine-tuning on a single-task, e.g. SST, but it this checkpoint is great for zero-shot classification and natural language inference (contradiction/entailment/neutral classification).
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| test_name | test_accuracy |
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| glue/mnli | 0.87 |
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