Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use chintagunta85/test_ner3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chintagunta85/test_ner3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chintagunta85/test_ner3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chintagunta85/test_ner3") model = AutoModelForTokenClassification.from_pretrained("chintagunta85/test_ner3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 954142d94fe6fd3f585c15e82879e3cacdb72a089fa1d8d7c4830b4f42f04ecf
- Size of remote file:
- 3.31 kB
- SHA256:
- f9aaf3813656aa2f1f540119ede35f6fa5d5a54a2d5268de9a351814fd61d746
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.