Text Classification
Transformers
Safetensors
English
modernbert
fact-verification
claim-verification
reward-model
llm-as-a-judge
distillation
decomposition
answerability
text-embeddings-inference
Instructions to use dipta007/question-judge-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipta007/question-judge-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipta007/question-judge-balanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipta007/question-judge-balanced") model = AutoModelForSequenceClassification.from_pretrained("dipta007/question-judge-balanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from dipta007/question-judge-balanced: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/dipta007/question-judge-balanced/resolve/main/tokenizer.json
- Command line
-
hf download hf://dipta007/question-judge-balanced/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/dipta007/question-judge-balanced/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.