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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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| gen_debiased_nli | 0.82 |
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```
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@inproceedings{sileo-2024-tasksource,
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title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
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# Model Card for Model ID
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ModernBERT mul-itask 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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| gen_debiased_nli | 0.82 |
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# Usage
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## [ZS] Zero-shot classification pipeline
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```python
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from transformers import pipeline
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classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-base-nli")
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text = "one day I will see the world"
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candidate_labels = ['travel', 'cooking', 'dancing']
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classifier(text, candidate_labels)
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```
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NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
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## [NLI] Natural language inference pipeline
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```python
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from transformers import pipeline
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pipe = pipeline("text-classification",model="tasksource/ModernBERT-base-nli")
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pipe([dict(text='there is a cat',
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text_pair='there is a black cat')]) #list of (premise,hypothesis)
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```
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# Citation
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```
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@inproceedings{sileo-2024-tasksource,
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title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
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