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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e67bd9c987bc2c1b68afbe892268eeea091b4f76e8f6f0f0b4f5d7568f4f32d
- Size of remote file:
- 3.58 kB
- SHA256:
- 89243d7b265e09483e6d0946d4b92058cfa2734ce5a4c5dfd5d975e18ed5d303
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