Instructions to use kwang2049/TSDAE-cqadupstack2nli_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kwang2049/TSDAE-cqadupstack2nli_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kwang2049/TSDAE-cqadupstack2nli_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kwang2049/TSDAE-cqadupstack2nli_stsb") model = AutoModel.from_pretrained("kwang2049/TSDAE-cqadupstack2nli_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kwang2049/TSDAE-cqadupstack2nli_stsb: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/kwang2049/TSDAE-cqadupstack2nli_stsb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kwang2049/TSDAE-cqadupstack2nli_stsb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kwang2049/TSDAE-cqadupstack2nli_stsb/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 01d173ddd19c71b5a461908e91a377b22a64ef909384fdbbf4099b84775347a2
- Size of remote file:
- 438 MB
- SHA256:
- 4d1a7eb004c2b07364efdbbd7c719d8d4aff9cc45fc1895f1c8eac14bcafd446
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