Instructions to use livinNector/IndicBERTv2-MLM-Sam-TLM-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use livinNector/IndicBERTv2-MLM-Sam-TLM-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="livinNector/IndicBERTv2-MLM-Sam-TLM-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("livinNector/IndicBERTv2-MLM-Sam-TLM-NER") model = AutoModelForTokenClassification.from_pretrained("livinNector/IndicBERTv2-MLM-Sam-TLM-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from livinNector/IndicBERTv2-MLM-Sam-TLM-NER: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/livinNector/IndicBERTv2-MLM-Sam-TLM-NER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://livinNector/IndicBERTv2-MLM-Sam-TLM-NER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/livinNector/IndicBERTv2-MLM-Sam-TLM-NER/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- b6999bb03f95e1c610b4b34a52daf5eca894420bfe54a1df8b1c27848a41f3b3
- Size of remote file:
- 1.11 GB
- SHA256:
- 0fbe52f71a8b51328f366a41a1440adad7c7e32d14c1b650253f155c5c2adc0d
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