Instructions to use mbruton/gal_ensp_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_ensp_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_ensp_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_ensp_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_ensp_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74aa4205b7e961ae8053b27a5a38948045f395aad211e7e0e2179047c7f46434
- Size of remote file:
- 2.22 GB
- SHA256:
- e7a805c6a4d89d9af0c5379d5f8025707c37092a39377306cafbe75eeef990c2
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