transformers tensorboard safetensors t5 text2text-generation generated_from_trainer dataset:samsum base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-small
datasets:
- samsum
library_name: transformers
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: finetuned-t5-for-samsum-summarization
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: validation
      args: samsum
    metrics:
    - type: rouge
      value: 0.4349
      name: Rouge1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuned-t5-for-samsum-summarization

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7032
- Rouge1: 0.4349
- Rouge2: 0.2083
- Rougel: 0.3675
- Rougelsum: 0.368
- Gen Len: 17.2885

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.2134        | 1.0   | 921  | 1.8395          | 0.4067 | 0.1789 | 0.3387 | 0.3392    | 16.9731 |
| 1.9967        | 2.0   | 1842 | 1.7883          | 0.4111 | 0.1859 | 0.3447 | 0.3451    | 16.9939 |
| 1.9323        | 3.0   | 2763 | 1.7518          | 0.4228 | 0.1912 | 0.3547 | 0.3549    | 17.2164 |
| 1.8926        | 4.0   | 3684 | 1.7444          | 0.425  | 0.1989 | 0.3576 | 0.3578    | 17.3166 |
| 1.8661        | 5.0   | 4605 | 1.7257          | 0.4266 | 0.1992 | 0.3607 | 0.361     | 17.0795 |
| 1.839         | 6.0   | 5526 | 1.7169          | 0.4318 | 0.2017 | 0.3614 | 0.3616    | 17.4132 |
| 1.8294        | 7.0   | 6447 | 1.7098          | 0.4317 | 0.2061 | 0.3662 | 0.3666    | 17.2848 |
| 1.8135        | 8.0   | 7368 | 1.7058          | 0.4329 | 0.2068 | 0.3657 | 0.3659    | 17.2812 |
| 1.8111        | 9.0   | 8289 | 1.7040          | 0.4324 | 0.2074 | 0.3666 | 0.3669    | 17.2213 |
| 1.8057        | 10.0  | 9210 | 1.7032          | 0.4349 | 0.2083 | 0.3675 | 0.368     | 17.2885 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
