Instructions to use harish/PT-v3-dev-test-all-PreTrain-e5-select with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/PT-v3-dev-test-all-PreTrain-e5-select with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/PT-v3-dev-test-all-PreTrain-e5-select")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/PT-v3-dev-test-all-PreTrain-e5-select") model = AutoModelForMaskedLM.from_pretrained("harish/PT-v3-dev-test-all-PreTrain-e5-select", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harish/PT-v3-dev-test-all-PreTrain-e5-select: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-select/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harish/PT-v3-dev-test-all-PreTrain-e5-select/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-select/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- b8dd3fd3a8a4194b6e02f56f4e62ad310ecf39abb5a820a586b7028e5e79ee6d
- Size of remote file:
- 712 MB
- SHA256:
- 43b20aab29531476bae3a666a85699a3d36c0f6ad6afb8dcc87ac0247fdcb517
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