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 flax_model.msgpack from DeepaPeri/MBERT-TRAINED-CONLL2002-es: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2002-es/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-es/resolve/main/flax_model.msgpack
709 MB
- Xet hash:
- 7a7cd8a6772f1c77cb9212e862d1eb3493072d127f9e693a3896668f66eda2f0
- Size of remote file:
- 709 MB
- SHA256:
- 70b7c50380d1947ea5d07c4f33adeb60e3991cc23f1db2b8b38cb3e877ca2e60
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