Instructions to use ai4bharat/IndicBERT-MLM-TLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai4bharat/IndicBERT-MLM-TLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ai4bharat/IndicBERT-MLM-TLM")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBERT-MLM-TLM") model = AutoModel.from_pretrained("ai4bharat/IndicBERT-MLM-TLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 643407984cd34e251d9fbf36b8db16057d614030fbd883a169810cdb0a31eb07
- Size of remote file:
- 1.11 GB
- SHA256:
- 09d134c17092d2e3dbf4996bf6cc6b8d22496950984dc7af7b4fd0c0f0eedd05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.