Instructions to use DeepaPeri/MBERT-TRAINED-CONLL2002-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepaPeri/MBERT-TRAINED-CONLL2002-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DeepaPeri/MBERT-TRAINED-CONLL2002-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2002-es") model = AutoModelForTokenClassification.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2002-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from DeepaPeri/MBERT-TRAINED-CONLL2002-es: direct link, hf CLI and curl.
- Browser
- Download file 24 Bytes
-
https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/README.md
- Command line
-
hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2002-es/README.md
-
curl -L -o README.md https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/README.md
24 Bytes
metadata
license: mit