transformers pytorch mt5 text2text-generation summarization urdu ur Abstractive Summarization generated_from_trainer dataset:xlsum license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- xlsum
license: apache-2.0
tags:
- summarization
- urdu
- ur
- mt5
- Abstractive Summarization
- generated_from_trainer
model-index:
- name: mt5-base-finetuned-urdu
  results: []
---

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# mt5-base-finetuned-urdu

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on Urdu subset the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8954
- Rouge-1: 28.84
- Rouge-2: 13.87
- Rouge-l: 25.63
- Gen Len: 19.0
- Bertscore: 71.31

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 3.6205        | 1.0   | 2114  | 3.0871          | 26.45   | 11.4    | 23.26   | 19.0    | 70.76     |
| 3.2169        | 2.0   | 4228  | 2.9830          | 27.19   | 11.91   | 23.95   | 19.0    | 70.92     |
| 3.0787        | 3.0   | 6342  | 2.9284          | 27.9    | 12.57   | 24.62   | 18.99   | 71.13     |
| 2.9874        | 4.0   | 8456  | 2.9049          | 28.28   | 12.91   | 24.99   | 18.99   | 71.28     |
| 2.9232        | 5.0   | 10570 | 2.8954          | 28.65   | 13.17   | 25.32   | 18.99   | 71.39     |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
