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 training_args.bin from henryscheible/stsb: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/stsb/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/stsb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/henryscheible/stsb/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 140e53f677894896d1e7dbbdc79706de8aec2b5370437f2dd6ee5b5bf0a2fd49
- Size of remote file:
- 3.38 kB
- SHA256:
- 6e4e1250bf8d76e708966ff4072d3ab33edb9f5b9ffe62d1dc88d90d97e298e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.