Token Classification
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use westbrook/bio_gpt_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use westbrook/bio_gpt_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="westbrook/bio_gpt_ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("westbrook/bio_gpt_ner") model = AutoModelForTokenClassification.from_pretrained("westbrook/bio_gpt_ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb8bbefbf8edf07916a53b9efc6179e112d9378f382d2f4153f07bcf55e662ef
- Size of remote file:
- 1.41 GB
- SHA256:
- 39f7fc476828dc1b120c3f8537bba5a4ed132b3ca20726b536e9774008a729d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.