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 pytorch_model.bin from tr3cks/bert-ner-es: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/tr3cks/bert-ner-es/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tr3cks/bert-ner-es/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tr3cks/bert-ner-es/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 61276c2958da2c89f0b62ef7dee2f34a1fb3b31b5b86e611a5b82cdf555c2123
- Size of remote file:
- 440 MB
- SHA256:
- 041b04ec3274e054b87c73fd510e58d843d7ef442c45356e52a4fe4b5e56a401
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