---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: cnn_dailymail_350_t5-base
  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. -->

# cnn_dailymail_350_t5-base

This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8973
- Rouge1: 0.2524
- Rouge2: 0.1238
- Rougel: 0.2084
- Rougelsum: 0.2083
- Gen Len: 18.9993

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.8756        | 0.45  | 500   | 0.9285          | 0.2483 | 0.1206 | 0.2051 | 0.2051    | 18.9993 |
| 0.8719        | 0.89  | 1000  | 0.9147          | 0.2496 | 0.1221 | 0.2063 | 0.2062    | 18.9999 |
| 0.8407        | 1.34  | 1500  | 0.9101          | 0.2497 | 0.1217 | 0.2061 | 0.2061    | 18.9999 |
| 0.8433        | 1.78  | 2000  | 0.9054          | 0.2512 | 0.1225 | 0.2072 | 0.2072    | 18.9995 |
| 0.8346        | 2.23  | 2500  | 0.9048          | 0.2515 | 0.123  | 0.2074 | 0.2074    | 18.9998 |
| 0.8308        | 2.67  | 3000  | 0.9037          | 0.2504 | 0.1226 | 0.2073 | 0.2073    | 18.9996 |
| 0.8189        | 3.12  | 3500  | 0.9022          | 0.2517 | 0.1232 | 0.2082 | 0.2081    | 19.0    |
| 0.8275        | 3.57  | 4000  | 0.9011          | 0.2514 | 0.123  | 0.2076 | 0.2076    | 19.0    |
| 0.8272        | 4.01  | 4500  | 0.9010          | 0.2517 | 0.1236 | 0.2081 | 0.2081    | 18.9993 |
| 0.819         | 4.46  | 5000  | 0.8994          | 0.2517 | 0.1235 | 0.208  | 0.2079    | 18.999  |
| 0.8096        | 4.9   | 5500  | 0.9001          | 0.2518 | 0.1236 | 0.208  | 0.208     | 18.9992 |
| 0.823         | 5.35  | 6000  | 0.8976          | 0.2519 | 0.1232 | 0.208  | 0.208     | 18.9993 |
| 0.8205        | 5.8   | 6500  | 0.8979          | 0.2516 | 0.1234 | 0.2079 | 0.2079    | 18.9996 |
| 0.8136        | 6.24  | 7000  | 0.8981          | 0.2515 | 0.1232 | 0.2078 | 0.2078    | 18.9992 |
| 0.8117        | 6.69  | 7500  | 0.8984          | 0.2519 | 0.1236 | 0.2081 | 0.208     | 18.9996 |
| 0.8039        | 7.13  | 8000  | 0.8979          | 0.2524 | 0.1237 | 0.2083 | 0.2083    | 18.9993 |
| 0.7934        | 7.58  | 8500  | 0.8981          | 0.2517 | 0.1235 | 0.2078 | 0.2078    | 18.9992 |
| 0.7947        | 8.02  | 9000  | 0.8979          | 0.252  | 0.1237 | 0.2081 | 0.2081    | 18.9989 |
| 0.8189        | 8.47  | 9500  | 0.8974          | 0.2523 | 0.1237 | 0.2083 | 0.2083    | 18.999  |
| 0.8102        | 8.92  | 10000 | 0.8976          | 0.2523 | 0.1237 | 0.2084 | 0.2084    | 18.9991 |
| 0.8029        | 9.36  | 10500 | 0.8978          | 0.2523 | 0.1237 | 0.2083 | 0.2083    | 18.9992 |
| 0.8004        | 9.81  | 11000 | 0.8973          | 0.2524 | 0.1238 | 0.2084 | 0.2083    | 18.9993 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0