Instructions to use AnanthZeke/NER-indic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnanthZeke/NER-indic-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnanthZeke/NER-indic-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AnanthZeke/NER-indic-bert") model = AutoModelForTokenClassification.from_pretrained("AnanthZeke/NER-indic-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from AnanthZeke/NER-indic-bert: direct link, hf CLI and curl.
- Browser
- Download file 15.3 MB
-
https://huggingface.co/AnanthZeke/NER-indic-bert/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://AnanthZeke/NER-indic-bert@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AnanthZeke/NER-indic-bert/resolve/refs%2Fpr%2F1/tokenizer.json
15.3 MB
- Xet hash:
- 031cba11df337338ac31ec0f2332fba7ada9d29c62a411a17c9222825f5c8fd8
- Size of remote file:
- 15.3 MB
- SHA256:
- 431ec72a485a382d7e3a6063af5f54249b36e4870e0c8d079918e79cd0a4f8f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.