Instructions to use mbruton/spa_enpt_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_enpt_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_enpt_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_enpt_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_enpt_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 424014b8cd0474f36ad3d9af0650b8303920a036a7195a5f3321399f95da5b91
- Size of remote file:
- 1.11 GB
- SHA256:
- fd6b9165bef5c3bf2a288f9e26c4c518d6cff7a02b99436a78e1b903345d370b
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