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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
base_model: t5-small
pipeline_tag: summarization
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: xsum
type: xsum
config: default
split: train[:10%]
args: default
metrics:
- type: rouge
value: 27.0616
name: Rouge1
---
<!-- 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-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5622
- Rouge1: 27.0616
- Rouge2: 6.8574
- Rougel: 21.1087
- Rougelsum: 21.1175
- Gen Len: 18.8246
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.8879 | 1.0 | 1148 | 2.6353 | 25.4786 | 5.8199 | 19.7404 | 19.7497 | 18.8089 |
| 2.8178 | 2.0 | 2296 | 2.5951 | 26.2963 | 6.4255 | 20.5395 | 20.5304 | 18.8084 |
| 2.7831 | 3.0 | 3444 | 2.5741 | 26.7181 | 6.7174 | 20.8888 | 20.8914 | 18.806 |
| 2.7572 | 4.0 | 4592 | 2.5647 | 27.0071 | 6.8335 | 21.108 | 21.1149 | 18.8202 |
| 2.7476 | 5.0 | 5740 | 2.5622 | 27.0616 | 6.8574 | 21.1087 | 21.1175 | 18.8246 |
### Framework versions
- Transformers 4.40.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1 |