Instructions to use mbruton/gal_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5eb53f4445f00b04b5f506a8c33ce86e8fa7d6b05dc551caae067422af74b19e
- Size of remote file:
- 1.11 GB
- SHA256:
- 8e7282b5e9c015bde671cfbed06f187a2026a856e35ac33195b484c47ff29fe5
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