Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-stsb") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-stsb") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9a18ac55e6cb909215f0a23d8f00b9a1585016b596c56a99ed283057e0e5423
- Size of remote file:
- 438 MB
- SHA256:
- d6fb01c54ec2177ce9b334c087b21974eb85c80343a64a7816d71d7a730317f9
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