Instructions to use mbruton/spa_enpt_mBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_enpt_mBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_enpt_mBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_enpt_mBERT") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_enpt_mBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 751a325626e72bdbf7695ca91d5dfc67e7b23aabd849c8dfe7baf4eab912d995
- Size of remote file:
- 1.42 GB
- SHA256:
- ba006e8ca9a069288b865bbfb8c11b7bd5893823cdabd13bce2a80461b89163c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.