transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google-t5/t5-base base_model:finetune:google-t5/t5-base license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-base
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: summarization
  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. -->

# summarization

This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2491
- Rouge1: 0.3279
- Rouge2: 0.2271
- Rougel: 0.3003
- Rougelsum: 0.3005
- Gen Len: 18.9811

## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.372         | 1.0   | 4189  | 0.2643          | 0.3326 | 0.2341 | 0.3055 | 0.3053    | 18.9784 |
| 0.3303        | 2.0   | 8378  | 0.2558          | 0.3379 | 0.2401 | 0.3112 | 0.3112    | 18.9808 |
| 0.3069        | 3.0   | 12567 | 0.2482          | 0.34   | 0.241  | 0.3129 | 0.313     | 18.9815 |
| 0.3057        | 4.0   | 16756 | 0.2491          | 0.3279 | 0.2271 | 0.3003 | 0.3005    | 18.9811 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
