transformers safetensors t5 text2text-generation generated_from_trainer base_model:VietAI/vit5-large-vietnews-summarization base_model:finetune:VietAI/vit5-large-vietnews-summarization license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: VietAI/vit5-large-vietnews-summarization
library_name: transformers
license: mit
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: SFTvit5-large_sum-10k_23Feb-2025
  results: []
---

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# SFTvit5-large_sum-10k_23Feb-2025

This model is a fine-tuned version of [VietAI/vit5-large-vietnews-summarization](https://huggingface.co/VietAI/vit5-large-vietnews-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4682
- Rouge1: 0.2555
- Rouge2: 0.177
- Rougel: 0.216
- Rougelsum: 0.2159
- Gen Len: 19.0

## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 0.9978 | 168  | 0.5023          | 0.2519 | 0.1711 | 0.2119 | 0.2118    | 19.0    |
| No log        | 1.9955 | 336  | 0.4599          | 0.254  | 0.1739 | 0.2142 | 0.2141    | 19.0    |
| No log        | 2.9993 | 505  | 0.4481          | 0.2554 | 0.1767 | 0.2166 | 0.2165    | 19.0    |
| No log        | 3.9970 | 673  | 0.4485          | 0.2561 | 0.1771 | 0.2161 | 0.216     | 19.0    |
| No log        | 4.9948 | 841  | 0.4508          | 0.2544 | 0.176  | 0.2154 | 0.2153    | 19.0    |
| 1.139         | 5.9985 | 1010 | 0.4544          | 0.2548 | 0.1767 | 0.2158 | 0.2158    | 19.0    |
| 1.139         | 6.9963 | 1178 | 0.4562          | 0.256  | 0.1789 | 0.2171 | 0.217     | 19.0    |
| 1.139         | 8.0    | 1347 | 0.4632          | 0.2558 | 0.1776 | 0.2164 | 0.2163    | 19.0    |
| 1.139         | 8.9978 | 1515 | 0.4668          | 0.2555 | 0.1773 | 0.2163 | 0.2163    | 19.0    |
| 1.139         | 9.9777 | 1680 | 0.4682          | 0.2555 | 0.177  | 0.216  | 0.2159    | 19.0    |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
