Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use autoevaluate/entity-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/entity-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="autoevaluate/entity-extraction")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/entity-extraction") model = AutoModelForTokenClassification.from_pretrained("autoevaluate/entity-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6f164bf95073ae74736975340a108a9587a9b573b9175873bb74aa91cddaec1
- Size of remote file:
- 266 MB
- SHA256:
- 42eab83d73a28e0d35668c03a5fb499aedaa890de0421dc026b039a3090313b7
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