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")# 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 tokenizer.json from ai4bharat/IndicBERTv2-MLM-Sam-TLM: direct link, hf CLI and curl.
- Browser
- Download file 7.75 MB
-
https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM/resolve/main/tokenizer.json
- Command line
-
hf download hf://ai4bharat/IndicBERTv2-MLM-Sam-TLM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM/resolve/main/tokenizer.json
7.75 MB
- Xet hash:
- 1a29a0f06a4d8739d74b852fb4cb8dc4969f881085bd0e221c854231a5c6732d
- Size of remote file:
- 7.75 MB
- SHA256:
- 6af22a5c4bc890322c365fcb77dd77c06cbb9b088ffa50db1892d5220313c495
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