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 training_args.bin from Maunish/ecomm-sbert: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/Maunish/ecomm-sbert/resolve/main/training_args.bin
- Command line
-
hf download hf://Maunish/ecomm-sbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Maunish/ecomm-sbert/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 1d237c1a34f2071066b5ebc90c87e7bffaae31575a779b83df9ebf0ebabf4579
- Size of remote file:
- 2.8 kB
- SHA256:
- badb9a7f45f2f274175a48e2578e84264c764d7e5c19435732b250836f5f5a8e
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