Instructions to use MERA-evaluation/MERA_Answer_judge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MERA-evaluation/MERA_Answer_judge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MERA-evaluation/MERA_Answer_judge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MERA-evaluation/MERA_Answer_judge") model = AutoModelForSequenceClassification.from_pretrained("MERA-evaluation/MERA_Answer_judge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from MERA-evaluation/MERA_Answer_judge: direct link, hf CLI and curl.
- Browser
- Download file 4.75 MB
-
https://huggingface.co/MERA-evaluation/MERA_Answer_judge/resolve/main/tokenizer.json
- Command line
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hf download hf://MERA-evaluation/MERA_Answer_judge/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/MERA-evaluation/MERA_Answer_judge/resolve/main/tokenizer.json
4.75 MB
File too large to display, you can check the raw version instead.