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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96016691f877a66822c0833ede3165b991caaa7bde1e26a1eda597cb65701a7e
- Size of remote file:
- 1.11 GB
- SHA256:
- 3a61a84f2484cd2cd40003004047d9e13f6d3bdb5655c8f07afbcebe4ca59036
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