Donut Base Finetuned Classification
This model is a fine-tuned version of LLMTestSaurav/donut-base-finetuned-classification on the imagefolder dataset.
Modellquelle
- Art
- CLIP vision
- Dateigröße
- 772 MB
- Basismodell
- LLMTestSaurav/donut-base-finetuned-classification
- Version
- vmodel
- Aktualisiert
- 1. Okt. 2026
- Downloads
- 4
- Quelle
- Hugging Face
Quellenbeschreibung
donut-base-finetuned-classification
This model is a fine-tuned version of LLMTestSaurav/donut-base-finetuned-classification on the imagefolder dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 30000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.56.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 1. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- library_name: transformers license: mit base_model: LLMTestSaurav/donut-base-finetuned-classification tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-finetuned-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-finetuned-classification This model is a fine-tuned version of [LLMTestSaurav/donut-base-finetuned-classification](https://huggingface.co/LLMTestSaurav/donut-base-finetuned-classification) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 30000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 154 downloads · 0 likes · Library transformers · Repo LLMTestSaurav/donut-base-finetuned-classification
Source context: 152 downloads · 0 likes · Library transformers · Repo LLMTestSaurav/donut-base-finetuned-classification
