Instructions to use raynardj/ner-chemical-bionlp-bc5cdr-pubmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raynardj/ner-chemical-bionlp-bc5cdr-pubmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raynardj/ner-chemical-bionlp-bc5cdr-pubmed")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raynardj/ner-chemical-bionlp-bc5cdr-pubmed") model = AutoModelForTokenClassification.from_pretrained("raynardj/ner-chemical-bionlp-bc5cdr-pubmed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2555947c1d3d5fd7ef46a572bc47812d79bd4b90e2c0b1d927ea856a87d2b009
- Size of remote file:
- 496 MB
- SHA256:
- db301fb868595dd5ce90278f86cf7a854f8e0ff12e720b7be89a87cc2d86c5ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.