Text Ranking
Transformers
Safetensors
English
qwen2
text-generation
judge-model
evaluation
reward-modeling
Instructions to use opencompass/CompassJudger-2-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opencompass/CompassJudger-2-32B-Instruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("opencompass/CompassJudger-2-32B-Instruct") model = AutoModelForCausalLM.from_pretrained("opencompass/CompassJudger-2-32B-Instruct") - Notebooks
- Google Colab
- Kaggle
Update pipeline tag to text-ranking and add descriptive tags
#3
by nielsr HF Staff - opened
This PR updates the pipeline_tag from text-generation to text-ranking to more accurately reflect the model's primary function as a judge model for evaluating and ranking LLM responses. This alignment improves discoverability for relevant use cases on the Hugging Face Hub.
Additionally, I have added more descriptive tags such as judge-model, evaluation, reward-modeling, and text-ranking, along with language: en, to further enhance the model's discoverability and categorization.
iridescentttt changed pull request status to merged