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README.md
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pipeline_tag: text-generation
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---
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# Swin-DistilBERTimbau
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**Swin-DistilBERTimbau** model trained on [**Flickr30K Portuguese**](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br) (translated version using Google Translator API)
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at resolution 224x224 and max sequence length of 512 tokens.
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## Model Description
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The Swin-DistilBERTimbau is a type of Vision Encoder Decoder which leverage the checkpoints of the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224)
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as encoder and the checkpoints of the [DistilBERTimbau](https://huggingface.co/adalbertojunior/distilbert-portuguese-cased) as decoder.
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The encoder checkpoints come from Swin Trasnformer version pre-trained on ImageNet-1k at resolution 224x224.
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The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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print(generated_text)
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```
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## Results
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The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore are abbreviated as C, B@4, RL, M and BS, respectively.
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|Model|Training|Evaluation|C|B@4|RL|M|BS|
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|-----|--------|----------|-------|------|-------|------|---------|
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|Swin-DistilBERTimbau|Flickr30K Portuguese|Flickr30K Portuguese|66.73|24.65|39.98|44.71|72.30|
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|Swin-
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## BibTeX entry and citation info
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```bibtex
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Coming Soon
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pipeline_tag: text-generation
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---
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# 🎉Swin-DistilBERTimbau
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**Swin-DistilBERTimbau** model trained on [**Flickr30K Portuguese**](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br) (translated version using Google Translator API)
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at resolution 224x224 and max sequence length of 512 tokens.
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## 🤖 Model Description
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The Swin-DistilBERTimbau is a type of Vision Encoder Decoder which leverage the checkpoints of the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224)
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as encoder and the checkpoints of the [DistilBERTimbau](https://huggingface.co/adalbertojunior/distilbert-portuguese-cased) as decoder.
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The encoder checkpoints come from Swin Trasnformer version pre-trained on ImageNet-1k at resolution 224x224.
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The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr. In this work, Swin-DistilBERTimbau
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was trained together with its buddy [Swin-GPorTuguese](https://huggingface.co/laicsiifes/swin-gpt2-flickr30k-pt-br).
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## 🧑💻 How to Get Started with the Model
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Use the code below to get started with the model.
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print(generated_text)
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```
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## 📈 Results
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The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore are abbreviated as C, B@4, RL, M and BS, respectively.
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|Model|Training|Evaluation|C|B@4|RL|M|BS|
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|-----|--------|----------|-------|------|-------|------|---------|
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|Swin-DistilBERTimbau|Flickr30K Portuguese|Flickr30K Portuguese|66.73|24.65|39.98|44.71|72.30|
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|Swin-GPorTuguese|Flickr30K Portuguese|Flickr30K Portuguese|64.71|23.15|39.39|44.36|71.70|
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## 📋 BibTeX entry and citation info
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```bibtex
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Coming Soon
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