Instructions to use ProdicusII/ZeroShotBioNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProdicusII/ZeroShotBioNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ProdicusII/ZeroShotBioNER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ProdicusII/ZeroShotBioNER") model = AutoModelForTokenClassification.from_pretrained("ProdicusII/ZeroShotBioNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ProdicusII/ZeroShotBioNER: direct link, hf CLI and curl.
- Browser
- Download file 559 Bytes
-
https://huggingface.co/ProdicusII/ZeroShotBioNER/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ProdicusII/ZeroShotBioNER/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ProdicusII/ZeroShotBioNER/resolve/main/tokenizer_config.json
559 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "name_or_path": "dmis-lab/biobert-v1.1", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/nikola.prodanovic.ivi/.cache/huggingface/hub/models--dmis-lab--biobert-v1.1/snapshots/551ca18efd7f052c8dfa0b01c94c2a8e68bc5488/special_tokens_map.json", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |