Instructions to use Salesforce/blip2-itm-vit-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip2-itm-vit-g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Salesforce/blip2-itm-vit-g") 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("Salesforce/blip2-itm-vit-g") model = AutoModelForZeroShotImageClassification.from_pretrained("Salesforce/blip2-itm-vit-g") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
#2
by nielsr HF Staff - opened
- special_tokens_map.json +35 -5
- tokenizer.json +0 -0
special_tokens_map.json
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"rstrip": false,
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"single_word": false
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},
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"cls_token":
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}
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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