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vit5-vietnamese-summarization-base
This model is a fine-tuned version of VietAI/vit5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7415
- Rouge1: 0.7545
- Rouge2: 0.4629
- Rougel: 0.4807
- Rougelsum: 0.4809
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 0.8025 | 1.0 | 675 | 0.8079 | 0.7224 | 0.4145 | 0.445 | 0.445 |
| 0.7254 | 2.0 | 1350 | 0.7613 | 0.7466 | 0.4464 | 0.4696 | 0.4697 |
| 0.6798 | 3.0 | 2025 | 0.7451 | 0.7527 | 0.4583 | 0.4788 | 0.479 |
| 0.6402 | 4.0 | 2700 | 0.7415 | 0.7545 | 0.4629 | 0.4807 | 0.4809 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("antechit03/vit5-vietnamese-summarization-base") model = AutoModelForMultimodalLM.from_pretrained("antechit03/vit5-vietnamese-summarization-base")