Instructions to use Salesforce/blip-vqa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-vqa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="Salesforce/blip-vqa-base")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") model = AutoModelForVisualQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
#1
by ybelkada - opened
- tokenizer_config.json +4 -0
tokenizer_config.json
CHANGED
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@@ -3,6 +3,10 @@
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "ybelkada/blip-image-captioning-base",
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"never_split": null,
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 512,
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"name_or_path": "ybelkada/blip-image-captioning-base",
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"never_split": null,
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