---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: finetuned-tamil-text-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. -->

# finetuned-tamil-text-summarization

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9838
- Rouge1: 0.1323
- Rouge2: 0.0864
- Rougel: 0.13
- Rougelsum: 0.1323

## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.6298        | 0.2   | 100  | 1.6144          | 0.0773 | 0.0611 | 0.0689 | 0.0833    |
| 0.4317        | 0.39  | 200  | 1.1760          | 0.1615 | 0.0699 | 0.1600 | 0.1600    |
| 0.2588        | 0.59  | 300  | 1.0634          | 0.08   | 0.0556 | 0.0841 | 0.0838    |
| 0.2665        | 0.79  | 400  | 1.0443          | 0.0631 | 0.0216 | 0.0631 | 0.0631    |
| 0.1972        | 0.99  | 500  | 1.0465          | 0.1909 | 0.1068 | 0.1793 | 0.1916    |
| 0.2041        | 1.19  | 600  | 0.9551          | 0.0904 | 0.0505 | 0.0968 | 0.0987    |
| 0.238         | 1.38  | 700  | 0.9423          | 0.1    | 0.0729 | 0.1    | 0.1033    |
| 0.275         | 1.58  | 800  | 0.9273          | 0.1467 | 0.1098 | 0.1504 | 0.1515    |
| 0.2379        | 1.78  | 900  | 0.9023          | 0.1    | 0.0833 | 0.1    | 0.1       |
| 0.2896        | 1.97  | 1000 | 0.9184          | 0.19   | 0.1    | 0.1889 | 0.1985    |
| 0.2663        | 2.17  | 1100 | 0.9003          | 0.0795 | 0.0678 | 0.0878 | 0.0833    |
| 0.237         | 2.37  | 1200 | 0.9139          | 0.1990 | 0.1029 | 0.1951 | 0.2062    |
| 0.2019        | 2.57  | 1300 | 0.9210          | 0.1128 | 0.0364 | 0.1128 | 0.1161    |
| 0.1794        | 2.77  | 1400 | 0.9038          | 0.1167 | 0.0864 | 0.1183 | 0.1206    |
| 0.1847        | 2.96  | 1500 | 0.8893          | 0.1434 | 0.1313 | 0.1438 | 0.1473    |
| 0.1436        | 3.16  | 1600 | 0.8872          | 0.1683 | 0.0583 | 0.1651 | 0.1729    |
| 0.138         | 3.36  | 1700 | 0.8929          | 0.2300 | 0.1249 | 0.2262 | 0.2312    |
| 0.1265        | 3.56  | 1800 | 0.9204          | 0.1745 | 0.0729 | 0.1700 | 0.1773    |
| 0.1828        | 3.75  | 1900 | 0.9094          | 0.18   | 0.1489 | 0.18   | 0.1862    |
| 0.1447        | 3.95  | 2000 | 0.8942          | 0.19   | 0.0989 | 0.1862 | 0.1962    |
| 0.099         | 4.15  | 2100 | 0.9297          | 0.2386 | 0.15   | 0.2352 | 0.2451    |
| 0.1366        | 4.35  | 2200 | 0.9124          | 0.12   | 0.0729 | 0.12   | 0.1245    |
| 0.1519        | 4.54  | 2300 | 0.9040          | 0.1873 | 0.0986 | 0.1833 | 0.1906    |
| 0.119         | 4.74  | 2400 | 0.9121          | 0.12   | 0.0458 | 0.1129 | 0.1229    |
| 0.1364        | 4.94  | 2500 | 0.9120          | 0.2090 | 0.1258 | 0.2067 | 0.2190    |
| 0.1           | 5.14  | 2600 | 0.9409          | 0.1251 | 0.0833 | 0.1240 | 0.1311    |
| 0.1683        | 5.34  | 2700 | 0.9423          | 0.1382 | 0.0951 | 0.1371 | 0.1417    |
| 0.1395        | 5.53  | 2800 | 0.9336          | 0.1612 | 0.1233 | 0.1600 | 0.1631    |
| 0.1067        | 5.73  | 2900 | 0.9290          | 0.2234 | 0.1316 | 0.2174 | 0.2169    |
| 0.1104        | 5.93  | 3000 | 0.9245          | 0.2    | 0.1    | 0.1915 | 0.1915    |
| 0.1474        | 6.13  | 3100 | 0.9423          | 0.2007 | 0.1030 | 0.1963 | 0.1985    |
| 0.1052        | 6.32  | 3200 | 0.9329          | 0.2023 | 0.1102 | 0.2000 | 0.2       |
| 0.1203        | 6.52  | 3300 | 0.9380          | 0.2023 | 0.1102 | 0.2000 | 0.2       |
| 0.1125        | 6.72  | 3400 | 0.9422          | 0.1896 | 0.0977 | 0.1862 | 0.19      |
| 0.1323        | 6.92  | 3500 | 0.9433          | 0.19   | 0.0977 | 0.1862 | 0.19      |
| 0.0949        | 7.11  | 3600 | 0.9529          | 0.1603 | 0.0945 | 0.1612 | 0.1599    |
| 0.1059        | 7.31  | 3700 | 0.9520          | 0.1383 | 0.0977 | 0.1419 | 0.1400    |
| 0.1482        | 7.51  | 3800 | 0.9514          | 0.2112 | 0.1205 | 0.2100 | 0.2073    |
| 0.1268        | 7.71  | 3900 | 0.9386          | 0.2038 | 0.1091 | 0.2015 | 0.2008    |
| 0.089         | 7.9   | 4000 | 0.9426          | 0.1508 | 0.1182 | 0.1562 | 0.1538    |
| 0.108         | 8.1   | 4100 | 0.9727          | 0.1383 | 0.1034 | 0.1445 | 0.1367    |
| 0.1292        | 8.3   | 4200 | 0.9640          | 0.2100 | 0.1256 | 0.2098 | 0.2098    |
| 0.0868        | 8.5   | 4300 | 0.9618          | 0.15   | 0.0943 | 0.1508 | 0.1465    |
| 0.1023        | 8.69  | 4400 | 0.9609          | 0.18   | 0.075  | 0.18   | 0.18      |
| 0.1102        | 8.89  | 4500 | 0.9644          | 0.1462 | 0.1    | 0.1512 | 0.145     |
| 0.1102        | 9.09  | 4600 | 0.9807          | 0.1262 | 0.0864 | 0.1362 | 0.1262    |
| 0.0942        | 9.29  | 4700 | 0.9866          | 0.1400 | 0.0977 | 0.1462 | 0.1400    |
| 0.129         | 9.49  | 4800 | 0.9853          | 0.1284 | 0.0864 | 0.1362 | 0.1295    |
| 0.0949        | 9.68  | 4900 | 0.9819          | 0.1911 | 0.0977 | 0.1962 | 0.1923    |
| 0.0852        | 9.88  | 5000 | 0.9852          | 0.1262 | 0.0864 | 0.1362 | 0.1262    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2