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-one-shot-better-prompt-enriched
  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-enriched

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: 2.6565
- Rouge: {'rouge1': 41.182, 'rouge2': 19.5265, 'rougeL': 18.933, 'rougeLsum': 18.933}
- Bert Score: 0.8721
- Bleurt 20: -0.8432
- Gen Len: 13.495

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 1.0658        | 1.0   | 172  | 2.5439          | {'rouge1': 42.7473, 'rouge2': 18.8594, 'rougeL': 19.0931, 'rougeLsum': 19.0931} | 0.875      | -0.8246   | 13.475  |
| 1.0299        | 2.0   | 344  | 2.6081          | {'rouge1': 41.8654, 'rouge2': 18.8191, 'rougeL': 19.2938, 'rougeLsum': 19.2938} | 0.8749     | -0.8044   | 13.59   |
| 0.9696        | 3.0   | 516  | 2.6143          | {'rouge1': 42.028, 'rouge2': 19.0578, 'rougeL': 19.2662, 'rougeLsum': 19.2662}  | 0.8742     | -0.8329   | 13.41   |
| 1.2399        | 4.0   | 688  | 2.4862          | {'rouge1': 45.2998, 'rouge2': 20.0367, 'rougeL': 19.5339, 'rougeLsum': 19.5339} | 0.8767     | -0.8051   | 14.13   |
| 1.1463        | 5.0   | 860  | 2.5147          | {'rouge1': 42.185, 'rouge2': 18.7982, 'rougeL': 19.3514, 'rougeLsum': 19.3514}  | 0.8748     | -0.8158   | 13.9    |
| 1.1535        | 6.0   | 1032 | 2.5190          | {'rouge1': 42.0433, 'rouge2': 18.2183, 'rougeL': 19.096, 'rougeLsum': 19.096}   | 0.8736     | -0.8365   | 13.245  |
| 1.105         | 7.0   | 1204 | 2.5545          | {'rouge1': 42.6564, 'rouge2': 18.934, 'rougeL': 19.1676, 'rougeLsum': 19.1676}  | 0.8741     | -0.8367   | 13.82   |
| 1.0948        | 8.0   | 1376 | 2.5909          | {'rouge1': 44.3364, 'rouge2': 19.3218, 'rougeL': 20.0526, 'rougeLsum': 20.0526} | 0.8756     | -0.8175   | 14.03   |
| 1.073         | 9.0   | 1548 | 2.5995          | {'rouge1': 43.7072, 'rouge2': 19.3837, 'rougeL': 19.3786, 'rougeLsum': 19.3786} | 0.8744     | -0.8409   | 14.035  |
| 1.0301        | 10.0  | 1720 | 2.5730          | {'rouge1': 42.6338, 'rouge2': 19.083, 'rougeL': 19.0249, 'rougeLsum': 19.0249}  | 0.8737     | -0.8464   | 13.695  |
| 1.0127        | 11.0  | 1892 | 2.6209          | {'rouge1': 41.7565, 'rouge2': 18.5013, 'rougeL': 18.7625, 'rougeLsum': 18.7625} | 0.8728     | -0.8639   | 13.55   |
| 1.0267        | 12.0  | 2064 | 2.6467          | {'rouge1': 43.4656, 'rouge2': 19.6808, 'rougeL': 19.491, 'rougeLsum': 19.491}   | 0.8736     | -0.8258   | 13.985  |
| 0.9901        | 13.0  | 2236 | 2.6401          | {'rouge1': 42.9025, 'rouge2': 20.2914, 'rougeL': 19.571, 'rougeLsum': 19.571}   | 0.8738     | -0.8341   | 13.91   |
| 0.9766        | 14.0  | 2408 | 2.6614          | {'rouge1': 42.9328, 'rouge2': 19.4599, 'rougeL': 19.6136, 'rougeLsum': 19.6136} | 0.8745     | -0.8085   | 13.855  |
| 1.0146        | 15.0  | 2580 | 2.6511          | {'rouge1': 42.2846, 'rouge2': 19.2036, 'rougeL': 19.0654, 'rougeLsum': 19.0654} | 0.8741     | -0.8262   | 13.565  |
| 0.9757        | 16.0  | 2752 | 2.6493          | {'rouge1': 42.1794, 'rouge2': 19.3274, 'rougeL': 18.711, 'rougeLsum': 18.711}   | 0.8717     | -0.8531   | 13.785  |
| 1.0131        | 17.0  | 2924 | 2.6542          | {'rouge1': 42.8968, 'rouge2': 19.6167, 'rougeL': 19.3472, 'rougeLsum': 19.3472} | 0.8731     | -0.8309   | 13.895  |
| 1.0183        | 18.0  | 3096 | 2.6541          | {'rouge1': 42.2663, 'rouge2': 19.5557, 'rougeL': 19.3909, 'rougeLsum': 19.3909} | 0.8726     | -0.8318   | 13.66   |
| 1.0028        | 19.0  | 3268 | 2.6581          | {'rouge1': 41.5487, 'rouge2': 19.7115, 'rougeL': 19.279, 'rougeLsum': 19.279}   | 0.8727     | -0.8381   | 13.56   |
| 1.0046        | 20.0  | 3440 | 2.6565          | {'rouge1': 41.182, 'rouge2': 19.5265, 'rougeL': 18.933, 'rougeLsum': 18.933}    | 0.8721     | -0.8432   | 13.495  |


### Framework versions

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