Instructions to use Red-8/Gujarati_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Red-8/Gujarati_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Red-8/Gujarati_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Red-8/Gujarati_NER") model = AutoModelForTokenClassification.from_pretrained("Red-8/Gujarati_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40bca03d8e1571c4e989db9d7b3c5450151d43967edd8432f887a20ffc1df398
- Size of remote file:
- 1.9 GB
- SHA256:
- e86810d5112c6df1ce33e099a32513efddfa2f8fe68152cbb428e6249562f50e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.