Instructions to use MLMvsCLM/610m-clm-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add GitHub link and relevant tags
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Adding a direct link to the associated GitHub repository (
https://github.com/Nicolas-BZRD/EuroBERT) in the "Overview" section. - Improving discoverability by adding more specific tags to the metadata, including
encoder,mlm,clm, andbiphasic, which accurately reflect the core research areas of the model presented in the paper.
These updates make it easier for users to find the code, understand the model's key characteristics, and improve its visibility on the Hugging Face Hub.