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

# test-dialogue-summarization

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: 2.2304
- Rouge: {'rouge1': 47.6559, 'rouge2': 23.5195, 'rougeL': 21.653, 'rougeLsum': 21.653}
- Bert Score: 0.8778
- Bleurt 20: -0.769
- Gen Len: 16.205

## 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
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge                                                                           | Bert Score | Bleurt 20 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 2.7518        | 1.0   | 186  | 2.4544          | {'rouge1': 42.0552, 'rouge2': 18.6296, 'rougeL': 20.1713, 'rougeLsum': 20.1713} | 0.8684     | -0.8842   | 16.4    |
| 2.5043        | 2.0   | 372  | 2.3359          | {'rouge1': 44.4236, 'rouge2': 20.2933, 'rougeL': 20.781, 'rougeLsum': 20.781}   | 0.8694     | -0.858    | 17.06   |
| 2.3625        | 3.0   | 558  | 2.2849          | {'rouge1': 42.6795, 'rouge2': 19.7272, 'rougeL': 20.5673, 'rougeLsum': 20.5673} | 0.8724     | -0.8485   | 16.0    |
| 2.1931        | 4.0   | 744  | 2.2602          | {'rouge1': 46.2739, 'rouge2': 21.51, 'rougeL': 21.0248, 'rougeLsum': 21.0248}   | 0.8749     | -0.8192   | 16.085  |
| 2.1187        | 5.0   | 930  | 2.2430          | {'rouge1': 44.6328, 'rouge2': 21.1871, 'rougeL': 20.8, 'rougeLsum': 20.8}       | 0.8729     | -0.8465   | 16.475  |
| 2.0406        | 6.0   | 1116 | 2.2199          | {'rouge1': 43.9237, 'rouge2': 21.0488, 'rougeL': 20.7538, 'rougeLsum': 20.7538} | 0.8724     | -0.8379   | 16.195  |
| 2.0104        | 7.0   | 1302 | 2.2111          | {'rouge1': 45.6132, 'rouge2': 21.7648, 'rougeL': 21.0134, 'rougeLsum': 21.0134} | 0.8738     | -0.8203   | 16.175  |
| 1.9662        | 8.0   | 1488 | 2.2029          | {'rouge1': 44.6747, 'rouge2': 21.4751, 'rougeL': 20.9398, 'rougeLsum': 20.9398} | 0.8728     | -0.8446   | 16.2    |
| 1.8518        | 9.0   | 1674 | 2.2129          | {'rouge1': 46.7682, 'rouge2': 22.4301, 'rougeL': 22.1849, 'rougeLsum': 22.1849} | 0.877      | -0.7737   | 16.445  |
| 1.8581        | 10.0  | 1860 | 2.2144          | {'rouge1': 46.788, 'rouge2': 22.5919, 'rougeL': 21.9838, 'rougeLsum': 21.9838}  | 0.8766     | -0.7886   | 16.175  |
| 1.805         | 11.0  | 2046 | 2.2126          | {'rouge1': 46.41, 'rouge2': 22.3295, 'rougeL': 21.6966, 'rougeLsum': 21.6966}   | 0.8771     | -0.7902   | 16.08   |
| 1.766         | 12.0  | 2232 | 2.2228          | {'rouge1': 48.3228, 'rouge2': 23.2358, 'rougeL': 22.2037, 'rougeLsum': 22.2037} | 0.8778     | -0.7648   | 16.42   |
| 1.7661        | 13.0  | 2418 | 2.2235          | {'rouge1': 47.3602, 'rouge2': 23.0001, 'rougeL': 22.0806, 'rougeLsum': 22.0806} | 0.8772     | -0.7872   | 16.205  |
| 1.689         | 14.0  | 2604 | 2.2284          | {'rouge1': 46.8864, 'rouge2': 22.952, 'rougeL': 21.6138, 'rougeLsum': 21.6138}  | 0.8784     | -0.7702   | 16.015  |
| 1.7035        | 15.0  | 2790 | 2.2165          | {'rouge1': 47.1586, 'rouge2': 23.3426, 'rougeL': 21.471, 'rougeLsum': 21.471}   | 0.8789     | -0.7622   | 15.945  |
| 1.7013        | 16.0  | 2976 | 2.2215          | {'rouge1': 47.0545, 'rouge2': 22.962, 'rougeL': 21.5717, 'rougeLsum': 21.5717}  | 0.879      | -0.7537   | 15.995  |
| 1.6886        | 17.0  | 3162 | 2.2276          | {'rouge1': 47.3071, 'rouge2': 23.0284, 'rougeL': 21.5429, 'rougeLsum': 21.5429} | 0.8781     | -0.758    | 16.07   |
| 1.6828        | 18.0  | 3348 | 2.2273          | {'rouge1': 47.2229, 'rouge2': 22.9743, 'rougeL': 21.756, 'rougeLsum': 21.756}   | 0.8777     | -0.7784   | 16.12   |
| 1.6164        | 19.0  | 3534 | 2.2286          | {'rouge1': 47.4937, 'rouge2': 23.2693, 'rougeL': 21.7418, 'rougeLsum': 21.7418} | 0.8771     | -0.7742   | 16.225  |
| 1.6247        | 20.0  | 3720 | 2.2304          | {'rouge1': 47.6559, 'rouge2': 23.5195, 'rougeL': 21.653, 'rougeLsum': 21.653}   | 0.8778     | -0.769    | 16.205  |


### Framework versions

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