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
- Xet hash:
- d870284da2c9118c6d276faf04fe9ccdde91c6e213aae16aa809301bdec2e171
- Size of remote file:
- 433 MB
- SHA256:
- a3dc5fd18fe52587add5c0725cd8dd10452c14dc7cd0571b0a481e1313742097
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.