Instructions to use Ludo33/eurobert210m_RSE_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/eurobert210m_RSE_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ludo33/eurobert210m_RSE_v1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ludo33/eurobert210m_RSE_v1", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("Ludo33/eurobert210m_RSE_v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ae600988c924b5748a009b84730953ff254624d85d74a6f3d2d3cba50edcd70
- Size of remote file:
- 849 MB
- SHA256:
- 82ff4f44e78fae0974d856b04ad00659747de2ba8a61b5b85fb8d1cab50d9f8d
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