Instructions to use shivangi/STS-B_64_128_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivangi/STS-B_64_128_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shivangi/STS-B_64_128_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shivangi/STS-B_64_128_output") model = AutoModelForSequenceClassification.from_pretrained("shivangi/STS-B_64_128_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shivangi/STS-B_64_128_output: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/shivangi/STS-B_64_128_output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shivangi/STS-B_64_128_output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shivangi/STS-B_64_128_output/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 0341c88caca63fb08c6f7029f537468882100427a998638de8ff0a99258e2ad7
- Size of remote file:
- 433 MB
- SHA256:
- 9b92da8ecdac3d972da752ba826644aaabf4eebf09e68edff97fb48b2b81f52a
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