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---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: t5-small_multinews_model
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-small_multinews_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6269
- Rouge Rouge1: 0.1471
- Rouge Rouge2: 0.0483
- Rouge Rougel: 0.1131
- Rouge Rougelsum: 0.1131
- Bleu Bleu: 0.0003
- Bleu Precisions: [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968]
- Bleu Brevity Penalty: 0.0022
- Bleu Length Ratio: 0.1408
- Bleu Translation Length: 191567
- Bleu Reference Length: 1360656
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge Rouge1 | Rouge Rouge2 | Rouge Rougel | Rouge Rougelsum | Bleu Bleu | Bleu Precisions | Bleu Brevity Penalty | Bleu Length Ratio | Bleu Translation Length | Bleu Reference Length |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:---------:|:-----------------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------------:|:---------------------:|
| 2.9189 | 1.0 | 7870 | 2.6869 | 0.1448 | 0.0474 | 0.1117 | 0.1117 | 0.0003 | [0.5827522821123012, 0.1820493433028088, 0.08242051182628926, 0.04574874477953644] | 0.0023 | 0.1411 | 192037 | 1360656 |
| 2.8435 | 2.0 | 15740 | 2.6535 | 0.1460 | 0.0474 | 0.1122 | 0.1122 | 0.0003 | [0.5809636959568958, 0.18126278620071182, 0.08254004826406995, 0.04636911719064694] | 0.0023 | 0.1410 | 191907 | 1360656 |
| 2.7922 | 3.0 | 23610 | 2.6389 | 0.1461 | 0.0477 | 0.1124 | 0.1124 | 0.0003 | [0.581669805398619, 0.18257649098318213, 0.08343485040444401, 0.0471782007379682] | 0.0022 | 0.1405 | 191160 | 1360656 |
| 2.814 | 4.0 | 31480 | 2.6280 | 0.1468 | 0.0478 | 0.1129 | 0.1129 | 0.0003 | [0.5844809737428239, 0.18360803285143726, 0.08381524001996615, 0.04753093788548009] | 0.0022 | 0.1406 | 191262 | 1360656 |
| 2.7869 | 5.0 | 39350 | 2.6269 | 0.1471 | 0.0483 | 0.1131 | 0.1131 | 0.0003 | [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968] | 0.0022 | 0.1408 | 191567 | 1360656 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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