Instructions to use ai4bharat/IndicBERTv2-MLM-Sam-TLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai4bharat/IndicBERTv2-MLM-Sam-TLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ai4bharat/IndicBERTv2-MLM-Sam-TLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBERTv2-MLM-Sam-TLM") model = AutoModelForMaskedLM.from_pretrained("ai4bharat/IndicBERTv2-MLM-Sam-TLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ai4bharat/IndicBERTv2-MLM-Sam-TLM: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ai4bharat/IndicBERTv2-MLM-Sam-TLM@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.12 GB
- Xet hash:
- 842672160f3840a09798b99fd7d5277d4ae548cbc836320629b4aaca18196fa4
- Size of remote file:
- 1.12 GB
- SHA256:
- b94cb947d2d9f46ccb05558967785f276bc61728b2a37fbade0cd054d5dcda7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.