transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google/flan-t5-small base_model:finetune:google/flan-t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google/flan-t5-small
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: t5-summarization-headers-50-epochs
  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. -->

# t5-summarization-headers-50-epochs

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2125
- Rouge: {'rouge1': 0.4117, 'rouge2': 0.2163, 'rougeL': 0.2158, 'rougeLsum': 0.2158}
- Bert Score: 0.8818
- Bleurt 20: -0.8026
- Gen Len: 14.46

## 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: 0.0001
- train_batch_size: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge                                                                       | Bert Score | Bleurt 20 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 3.0256        | 1.0   | 186  | 2.6300          | {'rouge1': 0.4643, 'rouge2': 0.1902, 'rougeL': 0.1973, 'rougeLsum': 0.1973} | 0.8664     | -0.8801   | 15.55   |
| 2.734         | 2.0   | 372  | 2.4218          | {'rouge1': 0.4489, 'rouge2': 0.2037, 'rougeL': 0.209, 'rougeLsum': 0.209}   | 0.8737     | -0.8686   | 14.995  |
| 2.5147        | 3.0   | 558  | 2.3219          | {'rouge1': 0.4363, 'rouge2': 0.1984, 'rougeL': 0.2067, 'rougeLsum': 0.2067} | 0.8742     | -0.8762   | 14.69   |
| 2.3007        | 4.0   | 744  | 2.2752          | {'rouge1': 0.4465, 'rouge2': 0.2043, 'rougeL': 0.2022, 'rougeLsum': 0.2022} | 0.8761     | -0.8603   | 14.625  |
| 2.1922        | 5.0   | 930  | 2.2331          | {'rouge1': 0.425, 'rouge2': 0.2033, 'rougeL': 0.2042, 'rougeLsum': 0.2042}  | 0.8779     | -0.829    | 14.87   |
| 2.1185        | 6.0   | 1116 | 2.2092          | {'rouge1': 0.4231, 'rouge2': 0.2096, 'rougeL': 0.2073, 'rougeLsum': 0.2073} | 0.8783     | -0.8359   | 14.68   |
| 2.0584        | 7.0   | 1302 | 2.1993          | {'rouge1': 0.4302, 'rouge2': 0.2114, 'rougeL': 0.2126, 'rougeLsum': 0.2126} | 0.8793     | -0.8202   | 15.015  |
| 2.0189        | 8.0   | 1488 | 2.1872          | {'rouge1': 0.4255, 'rouge2': 0.2086, 'rougeL': 0.2106, 'rougeLsum': 0.2106} | 0.879      | -0.8359   | 14.485  |
| 1.8933        | 9.0   | 1674 | 2.1967          | {'rouge1': 0.4307, 'rouge2': 0.2175, 'rougeL': 0.2165, 'rougeLsum': 0.2165} | 0.8821     | -0.7803   | 14.865  |
| 1.8859        | 10.0  | 1860 | 2.1905          | {'rouge1': 0.4342, 'rouge2': 0.2139, 'rougeL': 0.2193, 'rougeLsum': 0.2193} | 0.8828     | -0.7683   | 14.93   |
| 1.8395        | 11.0  | 2046 | 2.2006          | {'rouge1': 0.42, 'rouge2': 0.2135, 'rougeL': 0.2175, 'rougeLsum': 0.2175}   | 0.8815     | -0.7958   | 14.485  |
| 1.7848        | 12.0  | 2232 | 2.1970          | {'rouge1': 0.4309, 'rouge2': 0.2096, 'rougeL': 0.2171, 'rougeLsum': 0.2171} | 0.8826     | -0.8131   | 14.51   |
| 1.7855        | 13.0  | 2418 | 2.2026          | {'rouge1': 0.4218, 'rouge2': 0.2099, 'rougeL': 0.2182, 'rougeLsum': 0.2182} | 0.8812     | -0.8068   | 14.555  |
| 1.6971        | 14.0  | 2604 | 2.2006          | {'rouge1': 0.4035, 'rouge2': 0.2056, 'rougeL': 0.2109, 'rougeLsum': 0.2109} | 0.8816     | -0.817    | 14.145  |
| 1.7226        | 15.0  | 2790 | 2.2000          | {'rouge1': 0.413, 'rouge2': 0.2072, 'rougeL': 0.2145, 'rougeLsum': 0.2145}  | 0.8818     | -0.8106   | 14.415  |
| 1.7164        | 16.0  | 2976 | 2.2067          | {'rouge1': 0.4117, 'rouge2': 0.212, 'rougeL': 0.215, 'rougeLsum': 0.215}    | 0.8815     | -0.8198   | 14.235  |
| 1.6908        | 17.0  | 3162 | 2.2061          | {'rouge1': 0.4125, 'rouge2': 0.2193, 'rougeL': 0.2154, 'rougeLsum': 0.2154} | 0.8814     | -0.8089   | 14.37   |
| 1.6865        | 18.0  | 3348 | 2.2088          | {'rouge1': 0.4125, 'rouge2': 0.2173, 'rougeL': 0.217, 'rougeLsum': 0.217}   | 0.8819     | -0.807    | 14.46   |
| 1.6225        | 19.0  | 3534 | 2.2127          | {'rouge1': 0.4111, 'rouge2': 0.2161, 'rougeL': 0.2123, 'rougeLsum': 0.2123} | 0.8815     | -0.8039   | 14.425  |
| 1.6304        | 20.0  | 3720 | 2.2125          | {'rouge1': 0.4117, 'rouge2': 0.2163, 'rougeL': 0.2158, 'rougeLsum': 0.2158} | 0.8818     | -0.8026   | 14.46   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
