Text Classification
Transformers
Safetensors
English
modernbert
fact-verification
claim-verification
reward-model
llm-as-a-judge
distillation
decomposition
faithfulness
answer-verification
text-embeddings-inference
Instructions to use dipta007/answer-judge-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipta007/answer-judge-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipta007/answer-judge-balanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipta007/answer-judge-balanced") model = AutoModelForSequenceClassification.from_pretrained("dipta007/answer-judge-balanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Add full model card (description, arXiv, citation, usage, training) (#2)
- Add full model card (description, arXiv, citation, usage, training) (3c808464c897e769b330239626e4efe6b45dc40e) - Use official arXiv paper title in citation (cbe094b76fba1bd6a2b5dfeac96002e6e3aab5d8)e1a4d79
-
Upload ModernBertForSequenceClassification
Verified signature1e3872d
-
Upload ModernBertForSequenceClassification
Verified signaturef242829 -
Upload tokenizer
Verified signature554361d -
Upload ModernBertForSequenceClassification
Verified signatureec854c1 -
initial commit
Verified signature55fe4e0