Token Classification
Transformers
PyTorch
Graphcore
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use jimypbr/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimypbr/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jimypbr/test-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jimypbr/test-ner") model = AutoModelForTokenClassification.from_pretrained("jimypbr/test-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4555eb4fd120aaa3d834ea868a905dd762192c8b721b36cf180696e24c6cd3a1
- Size of remote file:
- 2.67 kB
- SHA256:
- 4d9297b4668e4ba63f799f096e5e8e8542b54ef206a1c816bc7ce7d25819fce3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.