transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google/flan-t5-base license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-summarization-one-shot-base-random
  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-base-random

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0562
- Rouge: {'rouge1': 44.8059, 'rouge2': 22.6975, 'rougeL': 22.1144, 'rougeLsum': 22.1144}
- Bert Score: 0.8826
- Bleurt 20: -0.6909
- Gen Len: 14.41

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 2.1793        | 1.0   | 601   | 1.9158          | {'rouge1': 43.6025, 'rouge2': 20.0332, 'rougeL': 20.768, 'rougeLsum': 20.768}   | 0.8742     | -0.8256   | 15.065  |
| 1.7262        | 2.0   | 1202  | 1.8547          | {'rouge1': 44.219, 'rouge2': 21.2046, 'rougeL': 21.6413, 'rougeLsum': 21.6413}  | 0.8822     | -0.7138   | 14.25   |
| 1.5456        | 3.0   | 1803  | 1.8743          | {'rouge1': 43.0548, 'rouge2': 20.0052, 'rougeL': 21.7232, 'rougeLsum': 21.7232} | 0.8799     | -0.7351   | 14.215  |
| 1.3864        | 4.0   | 2404  | 1.8856          | {'rouge1': 44.0749, 'rouge2': 22.1873, 'rougeL': 22.3386, 'rougeLsum': 22.3386} | 0.8824     | -0.721    | 14.265  |
| 1.1726        | 5.0   | 3005  | 1.9377          | {'rouge1': 42.5277, 'rouge2': 21.1762, 'rougeL': 22.0771, 'rougeLsum': 22.0771} | 0.8861     | -0.6948   | 13.84   |
| 1.0126        | 6.0   | 3606  | 2.0305          | {'rouge1': 43.3254, 'rouge2': 21.2322, 'rougeL': 21.9138, 'rougeLsum': 21.9138} | 0.8812     | -0.6905   | 14.17   |
| 0.9277        | 7.0   | 4207  | 2.0788          | {'rouge1': 44.4591, 'rouge2': 21.485, 'rougeL': 22.1901, 'rougeLsum': 22.1901}  | 0.8842     | -0.6869   | 14.15   |
| 0.8581        | 8.0   | 4808  | 2.1667          | {'rouge1': 43.3585, 'rouge2': 22.0956, 'rougeL': 22.6725, 'rougeLsum': 22.6725} | 0.8853     | -0.697    | 13.99   |
| 0.7611        | 9.0   | 5409  | 2.2544          | {'rouge1': 45.7618, 'rouge2': 22.8349, 'rougeL': 22.4909, 'rougeLsum': 22.4909} | 0.8826     | -0.6682   | 14.54   |
| 0.7624        | 10.0  | 6010  | 2.3085          | {'rouge1': 44.6569, 'rouge2': 21.8496, 'rougeL': 22.2368, 'rougeLsum': 22.2368} | 0.8818     | -0.684    | 14.42   |
| 0.5815        | 11.0  | 6611  | 2.4558          | {'rouge1': 44.248, 'rouge2': 22.0111, 'rougeL': 22.4011, 'rougeLsum': 22.4011}  | 0.884      | -0.6895   | 14.095  |
| 0.5842        | 12.0  | 7212  | 2.5537          | {'rouge1': 44.4124, 'rouge2': 22.0939, 'rougeL': 22.2455, 'rougeLsum': 22.2455} | 0.8846     | -0.6832   | 14.355  |
| 0.5936        | 13.0  | 7813  | 2.5306          | {'rouge1': 44.3422, 'rouge2': 22.7948, 'rougeL': 22.3682, 'rougeLsum': 22.3682} | 0.8838     | -0.7135   | 14.255  |
| 0.4445        | 14.0  | 8414  | 2.7685          | {'rouge1': 45.4309, 'rouge2': 23.2292, 'rougeL': 23.2752, 'rougeLsum': 23.2752} | 0.8826     | -0.6563   | 14.77   |
| 0.3908        | 15.0  | 9015  | 2.8443          | {'rouge1': 44.6809, 'rouge2': 22.1492, 'rougeL': 21.9333, 'rougeLsum': 21.9333} | 0.8828     | -0.6801   | 14.43   |
| 0.4475        | 16.0  | 9616  | 2.8570          | {'rouge1': 45.6488, 'rouge2': 22.8303, 'rougeL': 22.3293, 'rougeLsum': 22.3293} | 0.8846     | -0.6545   | 14.6    |
| 0.3963        | 17.0  | 10217 | 2.8927          | {'rouge1': 45.3239, 'rouge2': 22.4719, 'rougeL': 22.3093, 'rougeLsum': 22.3093} | 0.8838     | -0.6512   | 14.4    |
| 0.4013        | 18.0  | 10818 | 3.0375          | {'rouge1': 44.663, 'rouge2': 22.4292, 'rougeL': 21.7939, 'rougeLsum': 21.7939}  | 0.8845     | -0.6964   | 14.47   |
| 0.3355        | 19.0  | 11419 | 3.0206          | {'rouge1': 45.1714, 'rouge2': 23.0105, 'rougeL': 22.1146, 'rougeLsum': 22.1146} | 0.8829     | -0.6828   | 14.435  |
| 0.385         | 20.0  | 12020 | 3.0562          | {'rouge1': 44.8059, 'rouge2': 22.6975, 'rougeL': 22.1144, 'rougeLsum': 22.1144} | 0.8826     | -0.6909   | 14.41   |


### Framework versions

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