Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stsb") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/stsb: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/stsb/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/stsb@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/stsb/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- 16fd5e8732fb895b32fa62d1821a0e1046fa8d70153295e088324979f331fd93
- Size of remote file:
- 438 MB
- SHA256:
- b5d120648b2f2412261d7d408db613abfbc726985741768a9c4f8666b87f7dc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.