Instructions to use driftbench/stsb_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/stsb_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/stsb_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/stsb_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/stsb_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 021707d628aa3def99d3063e691e77d7c65f36b3b7fa1096f08c0089c0a796d1
- Size of remote file:
- 499 MB
- SHA256:
- e303066fc2f8dc628c8a28b07183d983dfae9ca7a91c45fac657ba598985b5db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.