Instructions to use Tirendaz/my_ner_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tirendaz/my_ner_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tirendaz/my_ner_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Tirendaz/my_ner_model") model = AutoModelForTokenClassification.from_pretrained("Tirendaz/my_ner_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3af5c86cb9282e84941de79ce8add28b08b00987b1439c07350959394519a6f6
- Size of remote file:
- 266 MB
- SHA256:
- ca37d71482ae9f4b490e97231a0e1dad323bed9baf7b03f75f81ec417c4c6843
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.