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 optimizer.pt from harish/PT-v3-dev-test-all-PreTrain-e5-select: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-select/resolve/main/optimizer.pt
- Command line
-
hf download hf://harish/PT-v3-dev-test-all-PreTrain-e5-select/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-select/resolve/main/optimizer.pt
1.42 GB
- Xet hash:
- 28b393a2b183111e05b6ba5973744009091684ab7f81386e33df04b756cbd82c
- Size of remote file:
- 1.42 GB
- SHA256:
- 70367492b35d750ebf5ca750bdd613400c0f1ff1b1812eefa1bec91769e4b6e3
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