Instructions to use Maunish/ecomm-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maunish/ecomm-sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Maunish/ecomm-sbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Maunish/ecomm-sbert") model = AutoModelForMaskedLM.from_pretrained("Maunish/ecomm-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Maunish/ecomm-sbert: direct link, hf CLI and curl.
- Browser
- Download file 91 MB
-
https://huggingface.co/Maunish/ecomm-sbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Maunish/ecomm-sbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Maunish/ecomm-sbert/resolve/main/pytorch_model.bin
91 MB
- Xet hash:
- 70a09535c046ab8b3016841ed65b851818866052d5655a83d15f7aac4ab494ec
- Size of remote file:
- 91 MB
- SHA256:
- 3be76e630adc080e706195c66077e4747ed45253845b68b6bd159c02bbda2943
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