Instructions to use rovargasc/SentenceBertV0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rovargasc/SentenceBertV0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rovargasc/SentenceBertV0")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rovargasc/SentenceBertV0") model = AutoModel.from_pretrained("rovargasc/SentenceBertV0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4aada6dda980d9203136f1cf01a02a73aa09a4e85831524429e133d9e710479
- Size of remote file:
- 499 MB
- SHA256:
- 631116eff556f37c079d35022147afc66867b4ace4e21d973aab6d596359bf09
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