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---
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language:
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- en
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- zh
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tags:
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- florence-2
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- document-vqa
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- image-text-retrieval
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- fine-tuned
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license: mit
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base_model: microsoft/Florence-2-base-ft
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---
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# adamchanadam/Test_Florence-2-FT-DocVQA
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This model is fine-tuned from [microsoft/Florence-2-base-ft](https://huggingface.co/microsoft/Florence-2-base-ft) for Document Visual Question Answering (DocVQA) tasks.
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## Model description
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- Fine-tuned for answering questions about images, specifically focused on logo recognition and company information.
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- The model uses the `<DocVQA>` prompt to indicate the task type.
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- Number of unique images: 28
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- Number of epochs: 7
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- Learning rate: 1e-06
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- Optimizer: AdamW
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- Early stopping: Patience of 2 epochs, delta of 0.0001
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Dataset statistics: Total number of questions for fine-tuning: 560.
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logo_recognition: 49 (8.75%) brand_identification: 48 (8.57%) visual_elements: 65 (11.61%) text_in_logo: 57 (10.18%) industry_classification: 49 (8.75%) product_service: 55 (9.82%) company_details: 89 (15.89%) negative_sample: 148 (26.43%)
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## Intended use & limitations
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- Use for answering questions about logos and company information in images
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- Performance may be limited for questions or image content not represented in the training data
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## Training procedure
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- Images were resized and normalized according to Florence-2's preprocessing standards.
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- The `<DocVQA>` prompt was used during fine-tuning to indicate the task type.
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- Questions and answers were provided for each image in the training set.
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- Batch size: 4
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- Evaluation metric: Cross-entropy loss on a held-out validation set
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For more information, please contact the model creators.
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