Instructions to use pucpr/clinicalnerpt-diagnostic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pucpr/clinicalnerpt-diagnostic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pucpr/clinicalnerpt-diagnostic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pucpr/clinicalnerpt-diagnostic") model = AutoModelForTokenClassification.from_pretrained("pucpr/clinicalnerpt-diagnostic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from pucpr/clinicalnerpt-diagnostic: direct link, hf CLI and curl.
- Browser
- Download file 151 Bytes
-
https://huggingface.co/pucpr/clinicalnerpt-diagnostic/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://pucpr/clinicalnerpt-diagnostic/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/pucpr/clinicalnerpt-diagnostic/resolve/main/tokenizer_config.json
151 Bytes
| {"do_lower_case": true, "max_len": 512, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |