Instructions to use cambridgeltl/trans-encoder-bi-simcse-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cambridgeltl/trans-encoder-bi-simcse-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cambridgeltl/trans-encoder-bi-simcse-bert-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/trans-encoder-bi-simcse-bert-large") model = AutoModel.from_pretrained("cambridgeltl/trans-encoder-bi-simcse-bert-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bf36b6a2ec00ba72bffd56e38bf95738d2358d743464e8ca08713c16a5067c8
- Size of remote file:
- 1.34 GB
- SHA256:
- 1b8e9dec9db2b8783fd913348410c88b2e446859cef7d2df37cf590dd26581f0
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