Token Classification
Transformers
PyTorch
English
named-entity-recognition
ner
span-ner
globalpointer
Instructions to use xinyacs/ecombert-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xinyacs/ecombert-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="xinyacs/ecombert-ner-v1")# Load model directly from transformers import EcomBertNER model = EcomBertNER.from_pretrained("xinyacs/ecombert-ner-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8f4104480d5c3e4b9f7da08f4bd54e926eb924a2c5ecd0df0e148b60bab8eed
- Size of remote file:
- 1.58 GB
- SHA256:
- 4f68f8b26a690304cb7dc1513c3107cc1919e2a0bd3b76b832b2695a89369fd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.