Text Classification
Transformers
Safetensors
English
modernbert
fact-verification
claim-verification
reward-model
llm-as-a-judge
distillation
decomposition
atomicity
text-embeddings-inference
Instructions to use dipta007/atomicity-grounded-judge-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipta007/atomicity-grounded-judge-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipta007/atomicity-grounded-judge-balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipta007/atomicity-grounded-judge-balanced") model = AutoModelForSequenceClassification.from_pretrained("dipta007/atomicity-grounded-judge-balanced") - Notebooks
- Google Colab
- Kaggle
Add full model card (description, arXiv, citation, usage, training)
#1
by dipta007 - opened
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dipta007 changed pull request status to merged