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
#4
by ybelkada - opened
- tokenizer_config.json +5 -1
tokenizer_config.json
CHANGED
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@@ -17,5 +17,9 @@
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]",
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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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}
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