Text Classification
Transformers
PyTorch
English
Chinese
internlm2
feature-extraction
Reward
RL
RFT
Reward Model
custom_code
Instructions to use internlm/POLAR-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/POLAR-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/POLAR-7B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add pipeline tag and library name to metadata
Verified signatureThis PR enhances the model card by adding the `pipeline_tag: text-ranking` and `library_name: transformers` to the YAML metadata. This improves the model's discoverability on the Hub by allowing users to filter by the "text-ranking" pipeline and enables the "Load with Transformers" widget for easier programmatic access.55cb02e
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