Instructions to use Prience91/ner_model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prience91/ner_model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Prience91/ner_model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Prience91/ner_model_output") model = AutoModelForTokenClassification.from_pretrained("Prience91/ner_model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Prience91/ner_model_output: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/Prience91/ner_model_output/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Prience91/ner_model_output/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Prience91/ner_model_output/resolve/main/tokenizer_config.json
299 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |