Feature Extraction
Transformers
PyTorch
English
distilbert
Information Retrieval
text-embeddings-inference
Instructions to use gzerveas/CODER-TAS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gzerveas/CODER-TAS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gzerveas/CODER-TAS-B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gzerveas/CODER-TAS-B") model = AutoModel.from_pretrained("gzerveas/CODER-TAS-B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e4dcb3e98751f649f62a3d94dbd33bc09aefaaab69faf4f3d4427ad94a5dfeb
- Size of remote file:
- 265 MB
- SHA256:
- 4df5b221b91b65992ae06923d079645fea59acb5e092892a79cc5b89528ed898
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