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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ed72090031869a6be67d702b912770489bbf544da6238647b572a3b58139ca3
- Size of remote file:
- 16.9 MB
- SHA256:
- 7e187627e5b5d6a7c87448a01756039db74c51c57092ea2420f433604b69a93a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.