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# mt5-small-finetuned-19jan-5

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: 2.6411
- Rouge1: 7.6385
- Rouge2: 0.3333
- Rougel: 7.4817
- Rougelsum: 7.4859

## 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 19.2402       | 1.0   | 60   | 8.2701          | 2.1815 | 0.1429 | 2.2246 | 2.2081    |
| 12.7954       | 2.0   | 120  | 5.3510          | 3.3524 | 0.3929 | 3.3843 | 3.3893    |
| 8.8288        | 3.0   | 180  | 3.5929          | 4.3158 | 0.4242 | 4.2947 | 4.2986    |
| 6.9994        | 4.0   | 240  | 3.2479          | 4.1515 | 0.5195 | 4.1991 | 4.1535    |
| 5.7594        | 5.0   | 300  | 3.0701          | 4.4127 | 0.4838 | 4.4044 | 4.4096    |
| 5.075         | 6.0   | 360  | 3.0252          | 5.6953 | 0.925  | 5.6925 | 5.6771    |
| 4.6336        | 7.0   | 420  | 2.9917          | 5.8009 | 1.1576 | 5.8699 | 5.871     |
| 4.3993        | 8.0   | 480  | 2.9676          | 5.8763 | 1.1953 | 5.9074 | 5.8808    |
| 4.1863        | 9.0   | 540  | 2.9213          | 6.2006 | 1.3455 | 6.2031 | 6.1713    |
| 4.0672        | 10.0  | 600  | 2.9115          | 5.3167 | 1.2394 | 5.3518 | 5.3606    |
| 3.9671        | 11.0  | 660  | 2.8743          | 5.2749 | 1.2394 | 5.3117 | 5.2936    |
| 3.86          | 12.0  | 720  | 2.8472          | 5.8311 | 1.1505 | 5.9026 | 5.8415    |
| 3.8103        | 13.0  | 780  | 2.8158          | 6.3536 | 1.1505 | 6.3989 | 6.3321    |
| 3.7412        | 14.0  | 840  | 2.7794          | 6.4438 | 1.1505 | 6.4702 | 6.4715    |
| 3.6757        | 15.0  | 900  | 2.7632          | 6.3778 | 0.9616 | 6.4342 | 6.417     |
| 3.643         | 16.0  | 960  | 2.7335          | 6.2346 | 0.9616 | 6.2724 | 6.2393    |
| 3.5952        | 17.0  | 1020 | 2.7152          | 5.9718 | 0.7727 | 6.0017 | 5.9683    |
| 3.585         | 18.0  | 1080 | 2.6998          | 8.8466 | 0.3333 | 8.7787 | 8.7648    |
| 3.493         | 19.0  | 1140 | 2.6982          | 8.1089 | 0.3333 | 7.95   | 7.9352    |
| 3.4807        | 20.0  | 1200 | 2.6911          | 7.9967 | 0.3333 | 7.8437 | 7.843     |
| 3.451         | 21.0  | 1260 | 2.6885          | 7.9967 | 0.3333 | 7.8437 | 7.843     |
| 3.4368        | 22.0  | 1320 | 2.6945          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
| 3.4044        | 23.0  | 1380 | 2.6909          | 8.6753 | 0.3333 | 8.5901 | 8.4835    |
| 3.3862        | 24.0  | 1440 | 2.6899          | 8.4263 | 0.3333 | 8.2222 | 8.1901    |
| 3.3421        | 25.0  | 1500 | 2.6897          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
| 3.3414        | 26.0  | 1560 | 2.6801          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
| 3.3354        | 27.0  | 1620 | 2.6772          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
| 3.299         | 28.0  | 1680 | 2.6780          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
| 3.3058        | 29.0  | 1740 | 2.6711          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
| 3.2678        | 30.0  | 1800 | 2.6693          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
| 3.2538        | 31.0  | 1860 | 2.6661          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
| 3.2361        | 32.0  | 1920 | 2.6687          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
| 3.2326        | 33.0  | 1980 | 2.6625          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
| 3.2142        | 34.0  | 2040 | 2.6648          | 8.0526 | 0.3333 | 7.9026 | 7.8801    |
| 3.1875        | 35.0  | 2100 | 2.6634          | 8.5204 | 0.3333 | 8.3199 | 8.3352    |
| 3.1717        | 36.0  | 2160 | 2.6611          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
| 3.1706        | 37.0  | 2220 | 2.6641          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
| 3.1541        | 38.0  | 2280 | 2.6573          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
| 3.1468        | 39.0  | 2340 | 2.6626          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
| 3.1376        | 40.0  | 2400 | 2.6602          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
| 3.1572        | 41.0  | 2460 | 2.6539          | 7.9385 | 0.3333 | 7.8019 | 7.8519    |
| 3.147         | 42.0  | 2520 | 2.6527          | 7.9385 | 0.3333 | 7.8019 | 7.8519    |
| 3.1199        | 43.0  | 2580 | 2.6487          | 7.9385 | 0.3333 | 7.8019 | 7.8519    |
| 3.1286        | 44.0  | 2640 | 2.6493          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.1042        | 45.0  | 2700 | 2.6519          | 8.1885 | 0.3333 | 7.9894 | 8.0292    |
| 3.099         | 46.0  | 2760 | 2.6525          | 8.1885 | 0.3333 | 7.9894 | 8.0292    |
| 3.1106        | 47.0  | 2820 | 2.6514          | 8.1885 | 0.3333 | 7.9894 | 8.0292    |
| 3.1036        | 48.0  | 2880 | 2.6501          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0934        | 49.0  | 2940 | 2.6501          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0822        | 50.0  | 3000 | 2.6435          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0858        | 51.0  | 3060 | 2.6479          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0825        | 52.0  | 3120 | 2.6455          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.063         | 53.0  | 3180 | 2.6437          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0641        | 54.0  | 3240 | 2.6429          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0703        | 55.0  | 3300 | 2.6430          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0554        | 56.0  | 3360 | 2.6413          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0498        | 57.0  | 3420 | 2.6415          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0668        | 58.0  | 3480 | 2.6411          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0657        | 59.0  | 3540 | 2.6409          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |
| 3.0591        | 60.0  | 3600 | 2.6411          | 7.6385 | 0.3333 | 7.4817 | 7.4859    |


### 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-5", "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-5
===========================


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: 2.6411
* Rouge1: 7.6385
* Rouge2: 0.3333
* Rougel: 7.4817
* Rougelsum: 7.4859


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: 10
* eval\_batch\_size: 10
* 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: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* 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: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* 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: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* 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