Instructions to use mbruton/spa_en_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_en_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_en_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_en_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_en_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25c75f7b93cc9b5cabc3b9aa28868a3e181b4de7c7a0387267c9b2fea6adf97b
- Size of remote file:
- 1.11 GB
- SHA256:
- 2a4c337a5bf6c5f728ad76ac3f9fda3119ff89bd91a58d0a5ce266e1df0c6377
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