Feature Extraction
Transformers
Safetensors
English
fuse_matcher
image-text-matching
flickr8k
cnn
vision-language
custom_code
Eval Results (legacy)
Instructions to use harpertoken/fuse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harpertoken/fuse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="harpertoken/fuse", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harpertoken/fuse", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -63,6 +63,10 @@ Two pairs from the canonical test set, scored by the published weights. Selectio
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| A blond woman in a blue shirt appears to wait for a ride . | 0.819 | match, correct |
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| A man in a pink shirt climbs a rock face | 0.938 | match, wrong |
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The non-match scores higher than the match and is classified wrong. That is not a chosen failure; it is what the rule produced, and it is consistent with 0.6933 test accuracy. These are two illustrations, not the metric.
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## Training
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| A blond woman in a blue shirt appears to wait for a ride . | 0.819 | match, correct |
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| A man in a pink shirt climbs a rock face | 0.938 | match, wrong |
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The actual canonical test images above, not recreations: the match on the left, the misclassified non-match on the right, with filenames and scores as measured.
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The non-match scores higher than the match and is classified wrong. That is not a chosen failure; it is what the rule produced, and it is consistent with 0.6933 test accuracy. These are two illustrations, not the metric.
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## Training
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