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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecf8fa9b043ad705665bf1278ae4777ec0a8a30527b193b5ab8f3a20d0f0cb37
- Size of remote file:
- 1.11 GB
- SHA256:
- 4b1105ff62e1d80b21722d6fba0b85eddaca94a4550f704cbf3be1f5c0c65a53
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