Instructions to use MLMvsCLM/610m-mlm40-12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm40-12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm40-12k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm40-12k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add comprehensive model card
#1
by nielsr HF Staff - opened
This PR adds a comprehensive model card for the model.
It includes:
- A link to the paper: Should We Still Pretrain Encoders with Masked Language Modeling?
- A link to the project page: https://hf.co/MLMvsCLM
- A link to the GitHub repository: https://github.com/Nicolas-BZRD/EuroBERT
- Relevant metadata (
license: apache-2.0,library_name: transformers,pipeline_tag: feature-extraction) for proper discoverability on the Hugging Face Hub. - A basic Python usage example for feature extraction.
This improves the model's documentation and usability.