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# mt5-small-finetuned-19jan-9

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6105
- Rouge1: 7.7
- Rouge2: 0.1667
- Rougel: 7.5759
- Rougelsum: 7.6113

## 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 16.5659       | 1.0   | 50   | 5.8214          | 2.2745 | 0.2338 | 2.2506 | 2.2815    |
| 10.2143       | 2.0   | 100  | 3.8680          | 4.4303 | 0.7671 | 4.4208 | 4.483     |
| 7.4492        | 3.0   | 150  | 3.2448          | 3.6533 | 0.6857 | 3.6861 | 3.6791    |
| 5.8239        | 4.0   | 200  | 3.0981          | 5.6287 | 0.8679 | 5.642  | 5.627     |
| 4.9377        | 5.0   | 250  | 3.0326          | 6.1068 | 1.1621 | 5.9651 | 6.0361    |
| 4.4824        | 6.0   | 300  | 2.9802          | 6.6496 | 1.3443 | 6.5332 | 6.5395    |
| 4.2193        | 7.0   | 350  | 2.9484          | 6.0845 | 1.2364 | 6.1077 | 6.0827    |
| 4.0234        | 8.0   | 400  | 2.9076          | 6.0958 | 1.3299 | 6.0806 | 6.0413    |
| 3.9046        | 9.0   | 450  | 2.8460          | 5.6462 | 1.1644 | 5.6397 | 5.6103    |
| 3.8087        | 10.0  | 500  | 2.8036          | 5.7538 | 1.1644 | 5.774  | 5.7442    |
| 3.6872        | 11.0  | 550  | 2.7727          | 6.5993 | 1.3311 | 6.5773 | 6.6049    |
| 3.6338        | 12.0  | 600  | 2.7285          | 6.0417 | 1.0778 | 6.047  | 6.089     |
| 3.574         | 13.0  | 650  | 2.7132          | 8.7833 | 0.25   | 8.803  | 8.6985    |
| 3.548         | 14.0  | 700  | 2.7023          | 8.9393 | 0.75   | 8.9619 | 8.8679    |
| 3.49          | 15.0  | 750  | 2.6943          | 9.1778 | 1.0    | 9.1537 | 9.0722    |
| 3.4098        | 16.0  | 800  | 2.6856          | 8.9167 | 0.75   | 8.9477 | 8.8597    |
| 3.3776        | 17.0  | 850  | 2.6827          | 8.3503 | 0.1667 | 8.3179 | 8.2614    |
| 3.3493        | 18.0  | 900  | 2.6899          | 8.6983 | 0.4524 | 8.6503 | 8.602     |
| 3.3309        | 19.0  | 950  | 2.6833          | 8.2433 | 0.4524 | 8.1185 | 8.1429    |
| 3.2833        | 20.0  | 1000 | 2.6785          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
| 3.2491        | 21.0  | 1050 | 2.6817          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
| 3.22          | 22.0  | 1100 | 2.6697          | 8.3829 | 0.4524 | 8.2852 | 8.3167    |
| 3.2433        | 23.0  | 1150 | 2.6522          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
| 3.1882        | 24.0  | 1200 | 2.6493          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
| 3.1622        | 25.0  | 1250 | 2.6630          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
| 3.1396        | 26.0  | 1300 | 2.6523          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
| 3.121         | 27.0  | 1350 | 2.6565          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
| 3.1095        | 28.0  | 1400 | 2.6385          | 8.5833 | 0.4524 | 8.45   | 8.516     |
| 3.1113        | 29.0  | 1450 | 2.6378          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
| 3.0661        | 30.0  | 1500 | 2.6415          | 8.2734 | 0.3333 | 8.1583 | 8.25      |
| 3.0316        | 31.0  | 1550 | 2.6435          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
| 3.0468        | 32.0  | 1600 | 2.6342          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
| 3.0323        | 33.0  | 1650 | 2.6330          | 7.8333 | 0.4167 | 7.7551 | 7.8317    |
| 3.0031        | 34.0  | 1700 | 2.6332          | 8.1192 | 0.4167 | 8.0718 | 8.1167    |
| 2.9904        | 35.0  | 1750 | 2.6291          | 8.2734 | 0.3333 | 8.1583 | 8.25      |
| 2.9765        | 36.0  | 1800 | 2.6364          | 7.8667 | 0.4167 | 7.8167 | 7.8269    |
| 2.9872        | 37.0  | 1850 | 2.6267          | 7.9984 | 0.4167 | 7.875  | 7.9843    |
| 2.976         | 38.0  | 1900 | 2.6252          | 7.9984 | 0.4167 | 7.875  | 7.9843    |
| 2.9528        | 39.0  | 1950 | 2.6319          | 7.701  | 0.3333 | 7.7167 | 7.6769    |
| 2.9385        | 40.0  | 2000 | 2.6279          | 7.8667 | 0.4167 | 7.8167 | 7.8269    |
| 2.9371        | 41.0  | 2050 | 2.6227          | 7.4658 | 0.4167 | 7.4167 | 7.4397    |
| 2.9214        | 42.0  | 2100 | 2.6172          | 8.1355 | 0.4167 | 8.0537 | 8.1329    |
| 2.9472        | 43.0  | 2150 | 2.6133          | 8.1355 | 0.4167 | 8.0537 | 8.1329    |
| 2.9215        | 44.0  | 2200 | 2.6101          | 7.4516 | 0.1667 | 7.3718 | 7.3647    |
| 2.9188        | 45.0  | 2250 | 2.6097          | 7.4516 | 0.1667 | 7.3718 | 7.3647    |
| 2.9003        | 46.0  | 2300 | 2.6089          | 7.4516 | 0.1667 | 7.3718 | 7.3647    |
| 2.8926        | 47.0  | 2350 | 2.6137          | 7.7769 | 0.1667 | 7.6692 | 7.7272    |
| 2.8872        | 48.0  | 2400 | 2.6118          | 7.7769 | 0.1667 | 7.6692 | 7.7272    |
| 2.8809        | 49.0  | 2450 | 2.6089          | 7.247  | 0.1667 | 7.151  | 7.1897    |
| 2.8676        | 50.0  | 2500 | 2.6027          | 7.2881 | 0.1667 | 7.1551 | 7.1947    |
| 2.8792        | 51.0  | 2550 | 2.6131          | 7.1382 | 0.1667 | 7.0703 | 7.0476    |
| 2.8705        | 52.0  | 2600 | 2.6144          | 7.7    | 0.1667 | 7.5759 | 7.6113    |
| 2.8887        | 53.0  | 2650 | 2.6130          | 7.7    | 0.1667 | 7.5759 | 7.6113    |
| 2.872         | 54.0  | 2700 | 2.6080          | 7.7    | 0.1667 | 7.5759 | 7.6113    |
| 2.8593        | 55.0  | 2750 | 2.6093          | 7.2881 | 0.1667 | 7.1551 | 7.1947    |
| 2.868         | 56.0  | 2800 | 2.6091          | 7.8387 | 0.1667 | 7.6729 | 7.7334    |
| 2.8729        | 57.0  | 2850 | 2.6096          | 7.8387 | 0.1667 | 7.6729 | 7.7334    |
| 2.8526        | 58.0  | 2900 | 2.6100          | 7.1382 | 0.1667 | 7.0703 | 7.0476    |
| 2.8671        | 59.0  | 2950 | 2.6105          | 7.7    | 0.1667 | 7.5759 | 7.6113    |
| 2.8544        | 60.0  | 3000 | 2.6105          | 7.7    | 0.1667 | 7.5759 | 7.6113    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-19jan-9", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-finetuned-19jan-9
===========================


This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6105
* Rouge1: 7.7
* Rouge2: 0.1667
* Rougel: 7.5759
* Rougelsum: 7.6113


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: 3e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 60


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 60 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 60 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 60### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2