Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use Gayu/my_sequence_labelling_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayu/my_sequence_labelling_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Gayu/my_sequence_labelling_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Gayu/my_sequence_labelling_model") model = AutoModelForTokenClassification.from_pretrained("Gayu/my_sequence_labelling_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86d8ae274ed052d9a6f93a9a8c2bbbf4fe1db73d1018be6f8fa55d4eb86e3782
- Size of remote file:
- 266 MB
- SHA256:
- 40bfe8c56ddb9fb5bbfb323b82ba39db39e19b5b4babe7dec44c8f52064136f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.