Image Classification
Transformers
PyTorch
TensorBoard
English
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/dit-base-Document_Classification-Desafio_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/dit-base-Document_Classification-Desafio_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/dit-base-Document_Classification-Desafio_1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("DunnBC22/dit-base-Document_Classification-Desafio_1") model = AutoModelForImageClassification.from_pretrained("DunnBC22/dit-base-Document_Classification-Desafio_1") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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language:
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# dit-base-Document_Classification-Desafio_1
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- Transformers 4.28.1
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- Pytorch 2.0.0
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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value: 0.9865
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language:
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license: mit
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---
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# dit-base-Document_Classification-Desafio_1
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- Transformers 4.28.1
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- Pytorch 2.0.0
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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## License Notice
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This model is a fine-tuned derivative of a pretrained model.
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Users must comply with the original model license.
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## Dataset Notice
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This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.
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