---
license: wtfpl
base_model: google/flan-t5-small
tags:
- summarization
language:
- en
metrics:
- rouge
datasets:
- csebuetnlp/xlsum
model-index:
- name: flan-t5-small-finetuned-xlsum-en-accelerate
  results: []
---

# Model Card for flan-t5-small-finetuned-xlsum-en-accelerate

This model is a fine-tuned version of [flan-t5-small](https://huggingface.co/google/flan-t5-small) on the csebuetnlp/xlsum dataset.

A reduced version of the English subset was used, focusing on shorter targets.

It achieves the following results on the evaluation set:
- rouge1: 29.99
- rouge2: 10.61
- rougeL: 25.52
- rougeLsum: 25.52

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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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Model can produce false information when summarizing.

This is very much an initial draft, and is not expected for use in production, use at your own risk.

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

| Epoch | rouge1 | rouge2 | rougeL | rougeLsum |
|:-----:|:------:|:------:|:------:|:---------:|
| 1.0   | 29.38  | 10.31  | 25.0   | 25.0      |
| 2.0   | 29.87  | 10.46  | 25.41  | 25.41     |
| 3.0   | 29.99  | 10.61  | 25.52  | 25.52     |

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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##### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@inproceedings{hasan-etal-2021-xl,
    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Islam, Md. Saiful  and
      Mubasshir, Kazi  and
      Li, Yuan-Fang  and
      Kang, Yong-Bin  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.413",
    pages = "4693--4703",
}
```

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**APA:**

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## Glossary [optional]

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## Model Card Contact

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