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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## 📈 Results
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The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore
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|Model|Training|Evaluation|C|B@4|RL|M|BS|
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## 📈 Results
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The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore
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(using [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)) 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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