Instructions to use arampacha/clip-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arampacha/clip-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="arampacha/clip-test") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("arampacha/clip-test") model = AutoModelForZeroShotImageClassification.from_pretrained("arampacha/clip-test") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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tags:
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- generated_from_trainer
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model-index:
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- name: clip-test
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results: []
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# clip-test
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on
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## Model description
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---
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tags:
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- generated_from_trainer
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datasets:
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- arampacha/rsicd
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model-index:
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- name: clip-test
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results: []
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# clip-test
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the arampacha/rsicd dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.2656
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## Model description
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