Feature Extraction
Transformers
Safetensors
chest2vec_embedding
text-embeddings
retrieval
radiology
chest
qwen
custom_code
Instructions to use lukeingawesome/chest2vec_0.6b_chest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukeingawesome/chest2vec_0.6b_chest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lukeingawesome/chest2vec_0.6b_chest", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lukeingawesome/chest2vec_0.6b_chest", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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