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")# 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
| license: mit | |
| # Zero- and Few-shot NER model | |
| ## Model description | |
| Model takes as input two strings. String1 is NER label. String1 must be phrase for entity. String2 is short text where String1 is searched for semantically. | |
| Model outputs list of zeros and ones corresponding to the occurance of NER and corresponing to tokens(tokens given by transformer tokenizer) of the Sring2, not to words. | |
| ## Citation |