Instructions to use mbruton/spa_pt_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_pt_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_pt_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_pt_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_pt_XLM-R") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb81335ca74d2f738c5b519b73724a56e889d2bcbc4a7c3dc72e111d009a8bfb
- Size of remote file:
- 1.11 GB
- SHA256:
- 9e3881b4100668e5c303185fa618ead53916b244abf63c549c6b89020d073045
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.