Token Classification
Transformers
PyTorch
English
english_ner_tr
ner
custom-code
english
custom_code
Instructions to use Ahmedhisham/EnglishNER_TR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmedhisham/EnglishNER_TR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ahmedhisham/EnglishNER_TR", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("Ahmedhisham/EnglishNER_TR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- ffea5654128de899c782cb0b83b691ca55e8de704ed5ffbfc5bf6e36c37aa343
- Size of remote file:
- 795 kB
- SHA256:
- b84f0d8828ec70bc4ea6a2847eb45b2800e2d2cba703636223f60d1a4bb12305
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.