Instructions to use l3cube-pune/mr-bert-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/mr-bert-embed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/mr-bert-embed")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/mr-bert-embed") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/mr-bert-embed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/mr-bert-embed: direct link, hf CLI and curl.
- Browser
- Download file 951 MB
-
https://huggingface.co/l3cube-pune/mr-bert-embed/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/mr-bert-embed/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/mr-bert-embed/resolve/main/pytorch_model.bin
951 MB
- Xet hash:
- 4a29df689c8a247968047cf2266c57b7aef1ea51bbf3392c6e51ad9730b47484
- Size of remote file:
- 951 MB
- SHA256:
- c087849c0ff8b4c597bbe53486e31eb0fcb42cb8afbe46083f9aa834ec4f86be
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