Instructions to use ModelTC/bert-base-uncased-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-stsb") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa55d4ad99d314e3237fea01a590777a9c37789794af6cfa454a7ae85b732564
- Size of remote file:
- 876 MB
- SHA256:
- f0ae11f0df38f5795865ba1d3997efb24b8729f301afe94832cdd5f36870272f
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