Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-large-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-large-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-large-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-large-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-large-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 513214a3ec7a9283419b29f93d97212ba55d676b6ac6409e71406fe895ee4b00
- Size of remote file:
- 1.33 GB
- SHA256:
- febce5a7915f2c894cdeb81cbe53bcc06ff3a8c22a96eaf9605aea0bbab172ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.