Image-to-Text
Transformers
Safetensors
Portuguese
vision-encoder-decoder
image-text-to-text
Eval Results (legacy)
Instructions to use laicsiifes/swin-distilbertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laicsiifes/swin-distilbertimbau with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="laicsiifes/swin-distilbertimbau")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("laicsiifes/swin-distilbertimbau") model = AutoModelForMultimodalLM.from_pretrained("laicsiifes/swin-distilbertimbau", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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-2](https://huggingface.co/laicsiifes/swin-gpt2-flickr30k-pt-br).
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Other models evaluated
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DeiT-DistilBERTimbau, DeiT-GPorTuguese-2, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese-2.
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## 🧑💻 How to Get Started with the Model
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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-2](https://huggingface.co/laicsiifes/swin-gpt2-flickr30k-pt-br).
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Other models evaluated did not perform as well as Swin-DistilBERTimbau and Swin-GPorTuguese-2, namely: DeiT-BERTimbau,
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DeiT-DistilBERTimbau, DeiT-GPorTuguese-2, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese-2.
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## 🧑💻 How to Get Started with the Model
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