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: t5-summarization-one-shot-better-prompt
  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-one-shot-better-prompt

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: 2.4802
- Rouge: {'rouge1': 37.3827, 'rouge2': 17.5806, 'rougeL': 20.1333, 'rougeLsum': 20.1333}
- Bert Score: 0.881
- Bleurt 20: -0.8056
- Gen Len: 13.305

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 2.8911        | 1.0   | 172  | 2.6479          | {'rouge1': 44.1146, 'rouge2': 17.635, 'rougeL': 19.7288, 'rougeLsum': 19.7288}  | 0.8758     | -0.8151   | 14.79   |
| 2.7068        | 2.0   | 344  | 2.5580          | {'rouge1': 42.4931, 'rouge2': 18.5074, 'rougeL': 19.757, 'rougeLsum': 19.757}   | 0.8774     | -0.8319   | 14.025  |
| 2.4884        | 3.0   | 516  | 2.5123          | {'rouge1': 43.5811, 'rouge2': 19.0798, 'rougeL': 20.1143, 'rougeLsum': 20.1143} | 0.8794     | -0.7737   | 14.455  |
| 2.3827        | 4.0   | 688  | 2.4875          | {'rouge1': 42.0721, 'rouge2': 18.4704, 'rougeL': 20.0131, 'rougeLsum': 20.0131} | 0.8801     | -0.7754   | 14.175  |
| 2.345         | 5.0   | 860  | 2.4596          | {'rouge1': 43.7021, 'rouge2': 20.0234, 'rougeL': 20.0962, 'rougeLsum': 20.0962} | 0.8809     | -0.7379   | 14.325  |
| 2.2438        | 6.0   | 1032 | 2.4466          | {'rouge1': 41.0624, 'rouge2': 18.8098, 'rougeL': 19.8672, 'rougeLsum': 19.8672} | 0.8803     | -0.7893   | 13.565  |
| 2.1878        | 7.0   | 1204 | 2.4505          | {'rouge1': 40.3802, 'rouge2': 18.9902, 'rougeL': 20.1633, 'rougeLsum': 20.1633} | 0.88       | -0.7735   | 13.26   |
| 2.084         | 8.0   | 1376 | 2.4384          | {'rouge1': 38.4615, 'rouge2': 18.3148, 'rougeL': 19.61, 'rougeLsum': 19.61}     | 0.8802     | -0.7813   | 13.235  |
| 2.096         | 9.0   | 1548 | 2.4380          | {'rouge1': 38.9264, 'rouge2': 18.2137, 'rougeL': 19.7464, 'rougeLsum': 19.7464} | 0.8794     | -0.8013   | 13.18   |
| 2.0251        | 10.0  | 1720 | 2.4445          | {'rouge1': 36.7486, 'rouge2': 17.2998, 'rougeL': 20.0546, 'rougeLsum': 20.0546} | 0.8807     | -0.8057   | 12.97   |
| 2.0139        | 11.0  | 1892 | 2.4449          | {'rouge1': 38.374, 'rouge2': 18.4603, 'rougeL': 20.3794, 'rougeLsum': 20.3794}  | 0.88       | -0.7885   | 13.415  |
| 1.957         | 12.0  | 2064 | 2.4502          | {'rouge1': 38.5085, 'rouge2': 18.9339, 'rougeL': 20.2576, 'rougeLsum': 20.2576} | 0.8812     | -0.7956   | 13.24   |
| 1.8508        | 13.0  | 2236 | 2.4510          | {'rouge1': 37.1308, 'rouge2': 17.4541, 'rougeL': 19.7904, 'rougeLsum': 19.7904} | 0.8802     | -0.7981   | 13.28   |
| 1.9315        | 14.0  | 2408 | 2.4590          | {'rouge1': 37.1896, 'rouge2': 17.8766, 'rougeL': 20.0, 'rougeLsum': 20.0}       | 0.8813     | -0.7869   | 13.205  |
| 1.8447        | 15.0  | 2580 | 2.4635          | {'rouge1': 38.7071, 'rouge2': 18.5335, 'rougeL': 20.5302, 'rougeLsum': 20.5302} | 0.8813     | -0.7887   | 13.505  |
| 1.8488        | 16.0  | 2752 | 2.4643          | {'rouge1': 38.0242, 'rouge2': 17.8906, 'rougeL': 20.3013, 'rougeLsum': 20.3013} | 0.8818     | -0.7789   | 13.23   |
| 1.7909        | 17.0  | 2924 | 2.4739          | {'rouge1': 37.1561, 'rouge2': 17.7929, 'rougeL': 20.0763, 'rougeLsum': 20.0763} | 0.8806     | -0.7882   | 13.22   |
| 1.8615        | 18.0  | 3096 | 2.4770          | {'rouge1': 37.2891, 'rouge2': 17.6695, 'rougeL': 19.951, 'rougeLsum': 19.951}   | 0.8803     | -0.8131   | 13.305  |
| 1.7938        | 19.0  | 3268 | 2.4796          | {'rouge1': 37.1339, 'rouge2': 17.6046, 'rougeL': 20.1063, 'rougeLsum': 20.1063} | 0.881      | -0.8031   | 13.26   |
| 1.7814        | 20.0  | 3440 | 2.4802          | {'rouge1': 37.3827, 'rouge2': 17.5806, 'rougeL': 20.1333, 'rougeLsum': 20.1333} | 0.881      | -0.8056   | 13.305  |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
