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:
- 3173aaa5d20e064b5787ec6f84419ca41c514c3a966c581b79e843e9d4cfebca
- Size of remote file:
- 1.11 GB
- SHA256:
- 8efa911e5b5aae9f5545421ab880ea4cd7027e5087aefd7d162bc5395bc866ef
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