Instructions to use tr3cks/bert-ner-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tr3cks/bert-ner-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tr3cks/bert-ner-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tr3cks/bert-ner-es") model = AutoModelForTokenClassification.from_pretrained("tr3cks/bert-ner-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tr3cks/bert-ner-es: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/tr3cks/bert-ner-es/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://tr3cks/bert-ner-es/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tr3cks/bert-ner-es/resolve/main/flax_model.msgpack
437 MB
- Xet hash:
- 9d14aba37d5140a12dffe55fd6f8039dd4cb61cfcea879dafcb3e7f542da6c50
- Size of remote file:
- 437 MB
- SHA256:
- b45cdb3db1219e25ceda73fe7b117731aae2193e6bc98767e490345bcd20841b
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