Instructions to use mbruton/gal_en_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_en_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_en_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_en_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_en_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c01b2cff730fc8115b4b1bb3800b585385d697f46555fe664481b39dcd0da87d
- Size of remote file:
- 2.22 GB
- SHA256:
- cdca9ab2399e0b84824d48ddd75aab82c8f87a8180c5526139d2b35b53a3d049
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