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")# 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
- Xet hash:
- 4a29df689c8a247968047cf2266c57b7aef1ea51bbf3392c6e51ad9730b47484
- Size of remote file:
- 951 MB
- SHA256:
- c087849c0ff8b4c597bbe53486e31eb0fcb42cb8afbe46083f9aa834ec4f86be
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