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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tr3cks/bert-ner-es") model = AutoModelForTokenClassification.from_pretrained("tr3cks/bert-ner-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d14aba37d5140a12dffe55fd6f8039dd4cb61cfcea879dafcb3e7f542da6c50
- Size of remote file:
- 437 MB
- SHA256:
- b45cdb3db1219e25ceda73fe7b117731aae2193e6bc98767e490345bcd20841b
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