Instructions to use mbruton/spa_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4d9aea0924a87b057b32dfdbbf5d6414f63f0945b046bad29bc7678b7e848f0
- Size of remote file:
- 623 Bytes
- SHA256:
- 85cede8b2bc6fc8dfa7bc8d7c79b562f7ab188fc2fa4824c8a9a2c895dc10fea
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