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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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