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 pytorch_model.bin from DeepaPeri/MBERT-TRAINED-CONLL2002-es: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2002-es/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- e0d07bf062594117e5133f77f450456470f51221014ba059ae89608f4e77c1b1
- Size of remote file:
- 711 MB
- SHA256:
- 0e4d13c2f858a569580ecfcc341a0404e2357218f90dcbf2ee86d093fea3c40f
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