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--- |
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tags: |
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- image-to-text |
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- image-captioning |
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license: apache-2.0 |
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metrics: |
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- rouge |
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datasets: |
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- nlphuji/flickr30k |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
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example_title: Savanna |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
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example_title: Football Match |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg |
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example_title: Airport |
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base_model: |
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- google/vit-base-patch16-224-in21k |
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model-index: |
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- name: mozilla/distilvit |
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results: |
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- task: |
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type: image-to-text |
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name: Image To Text |
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dataset: |
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name: nlphuji/flickr30k |
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type: nlphuji/flickr30k |
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metrics: |
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- name: ROUGE-1 |
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type: rouge |
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value: 43.006 |
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verified: true |
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- name: ROUGE-2 |
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type: rouge |
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value: 16.9939 |
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verified: true |
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- name: ROUGE-L |
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type: rouge |
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value: 38.8923 |
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verified: true |
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- name: ROUGE-LSUM |
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type: rouge |
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value: 38.8877 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.19939416646957397 |
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- name: gen_len |
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type: gen_len |
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value: 11.327256736227712 |
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verified: true |
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--- |
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# distilvit |
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This model is a work in progress. Fine-tuned version of those base models: |
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- a VIT model for the image encoder: https://huggingface.co/google/vit-base-patch16-224-in21k |
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- a Distilled GPT-2 model for the text decoder: https://huggingface.co/distilbert/distilgpt2 |
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This model was trained on: |
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- Flickr30k : https://huggingface.co/datasets/nlphuji/flickr30k |
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- COCO 2017: https://cocodataset.org |
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You can get that checkpoint using the 3083a3cef6e3c8dd90df3f088074bbe836b0f403 commit. |
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It was then further fine-tuned on : |
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- Flickr30k debiased: https://huggingface.co/datasets/Mozilla/flickr30k-transformed-captions |
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- DocOrNot: https://huggingface.co/datasets/Mozilla/docornot |
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You can find the code used to create the model here: https://github.com/mozilla/distilvit |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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