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:
- 8e0919fddbe026a6f4cfc3cc75bc1db0b00b7e3cdb9422543e7d8a7adea7683e
- Size of remote file:
- 1.11 GB
- SHA256:
- e1c0f44539d687cfda4c71a0677ec624f6fda506537e661a8b776323f1a8703e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.