Instructions to use google/owlvit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlvit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="google/owlvit-base-patch32")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlvit-base-patch32") - Notebooks
- Google Colab
- Kaggle
Alara Dirik commited on
Commit ·
aa665fc
1
Parent(s): 5fd7f74
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -2
tokenizer_config.json
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"single_word": false
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},
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"errors": "replace",
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"model_max_length":
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"name_or_path": "openai/clip-vit-base-patch32",
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"pad_token": "!",
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"processor_class": "OwlViTProcessor",
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"special_tokens_map_file": "/Users/adirik/.cache/huggingface/transformers/18a566598f286c9139f88160c99f84eec492a26bd22738fa9cb44d5b7e0a5c76.cce1206abbad28826f000510f22f354e53e66a97f7c23745a7dfe27609cc07f5",
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"tokenizer_class": "CLIPTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"single_word": false
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},
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"errors": "replace",
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"model_max_length": 16,
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"name_or_path": "openai/clip-vit-base-patch32",
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"pad_token": "!",
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"processor_class": "OwlViTProcessor",
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"tokenizer_class": "CLIPTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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