Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceM4/DocumentVQA
+language:
+- en
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+# Florence-2-finetuned-HuggingFaceM4-DOcumentVQA
+
+This model is a fine-tuned version of [microsoft/Florence-2-base-ft](https://huggingface.co/microsoft/Florence-2-base-ft) on [HuggingFaceM4/DocumentVQA](https://huggingface.co/datasets/HuggingFaceM4/DocumentVQA) dataset.
+
+It is the result of the post [Fine tuning Florence-2](https://maximofn.com/fine-tuning-florence-2/)
+
+It achieves the following results on the evaluation set:
+- Loss: 0.7168
+
+## Model description
+
+Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.
+
+He has also been finetuned in the docVQA task.
+
+## Training and evaluation data
+
+This is finetuned on [HuggingFaceM4/DocumentVQA](https://huggingface.co/datasets/HuggingFaceM4/DocumentVQA) dataset.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-6
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Validation Loss |
+|:-------------:|:-----:|:---------------:|
+| 1.1535        | 1.0   | 0.7698          |
+| 0.6530        | 2.0   | 0.7253          |
+| 0.5878        | 3.0   | 0.7168          |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1