Instructions to use deepset/gbert-large-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-large-sts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/gbert-large-sts")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/gbert-large-sts") model = AutoModelForSequenceClassification.from_pretrained("deepset/gbert-large-sts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d33f6d5f315b621da29bc743067a3e4c7ced4e8a6f9b868615a0faf07d4a0940
- Size of remote file:
- 1.34 GB
- SHA256:
- 952dc316be0841464a89e8710b7f42ef2b936c2d128451b38cfa8d556edc5bc4
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