Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use param-bharat/ModernBERT-large-nli-scorer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use param-bharat/ModernBERT-large-nli-scorer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="param-bharat/ModernBERT-large-nli-scorer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("param-bharat/ModernBERT-large-nli-scorer") model = AutoModelForSequenceClassification.from_pretrained("param-bharat/ModernBERT-large-nli-scorer") - Notebooks
- Google Colab
- Kaggle
Upload NLIScorer
Browse files- pipeline.py +1 -1
pipeline.py
CHANGED
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@@ -365,7 +365,7 @@ class NLIScorer(Pipeline):
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postprocess_kwargs = {}
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if "task_type" in kwargs:
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preprocess_kwargs["task_type"] = kwargs["task_type"]
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return preprocess_kwargs, {}, postprocess_kwargs
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def preprocess(self, inputs, task_type):
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postprocess_kwargs = {}
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if "task_type" in kwargs:
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preprocess_kwargs["task_type"] = kwargs["task_type"]
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postprocess_kwargs["task_type"] = kwargs["task_type"]
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return preprocess_kwargs, {}, postprocess_kwargs
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def preprocess(self, inputs, task_type):
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