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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c685a1c84b57624c82e9750f9e91508720dcf99225a371608d3071ab16c3db01
- Size of remote file:
- 1.11 GB
- SHA256:
- fee26730e6223a8d4f13870fc7f4b8eb0f803e7165b819238d96f4fb8589e7b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.