transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: t5-small
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: bbc_news_summarization
  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. -->

# bbc_news_summarization

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3542
- Rouge1: 0.1923
- Rouge2: 0.1312
- Rougel: 0.1729
- Rougelsum: 0.1729
- Gen Len: 19.0

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 112  | 0.4972          | 0.1972 | 0.1415 | 0.18   | 0.1796    | 19.0    |
| No log        | 2.0   | 224  | 0.3819          | 0.1898 | 0.1286 | 0.1709 | 0.1708    | 19.0    |
| No log        | 3.0   | 336  | 0.3603          | 0.1916 | 0.1304 | 0.172  | 0.1722    | 19.0    |
| No log        | 4.0   | 448  | 0.3542          | 0.1923 | 0.1312 | 0.1729 | 0.1729    | 19.0    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
