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 tokenizer_config.json from tr3cks/bert-ner-es: direct link, hf CLI and curl.
- Browser
- Download file 258 Bytes
-
https://huggingface.co/tr3cks/bert-ner-es/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://tr3cks/bert-ner-es/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/tr3cks/bert-ner-es/resolve/main/tokenizer_config.json
258 Bytes
| {"do_lower_case": false, "special_tokens_map_file": "C:\\Users\\Javi/.cache\\torch\\transformers\\aa40d465a73f3614a619f68336225bb02e1d0917937da285039a618a5135724d.275045728fbf41c11d3dae08b8742c054377e18d92cc7b72b6351152a99b64e4", "full_tokenizer_file": null} |