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# mt5-small-finetuned-19jan-7

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: 2.6123
- Rouge1: 6.8298
- Rouge2: 0.1667
- Rougel: 6.5947
- Rougelsum: 6.6685

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 16.2953       | 1.0   | 50   | 5.4420          | 2.3065 | 0.0    | 2.3217 | 2.3089    |
| 10.6895       | 2.0   | 100  | 4.4691          | 3.2975 | 0.3693 | 3.2976 | 3.3376    |
| 7.0377        | 3.0   | 150  | 3.2638          | 4.1896 | 0.3485 | 4.1487 | 4.1878    |
| 5.7221        | 4.0   | 200  | 3.0772          | 6.2012 | 0.7955 | 6.1846 | 6.3083    |
| 4.9356        | 5.0   | 250  | 3.0312          | 5.2032 | 0.8545 | 5.1829 | 5.2263    |
| 4.4656        | 6.0   | 300  | 3.0022          | 5.6901 | 1.3505 | 5.6184 | 5.6791    |
| 4.2279        | 7.0   | 350  | 2.9585          | 5.6907 | 1.5424 | 5.644  | 5.7768    |
| 4.0578        | 8.0   | 400  | 2.9098          | 5.7425 | 1.0202 | 5.6452 | 5.7881    |
| 3.9236        | 9.0   | 450  | 2.8686          | 6.2001 | 1.1793 | 6.1891 | 6.2508    |
| 3.8237        | 10.0  | 500  | 2.8222          | 5.9182 | 1.1793 | 5.8436 | 5.9807    |
| 3.7078        | 11.0  | 550  | 2.7890          | 5.4733 | 1.3896 | 5.3702 | 5.4957    |
| 3.641         | 12.0  | 600  | 2.7522          | 5.8312 | 1.1793 | 5.784  | 5.9037    |
| 3.5527        | 13.0  | 650  | 2.7168          | 6.3129 | 1.1793 | 6.2924 | 6.384     |
| 3.5281        | 14.0  | 700  | 2.7000          | 9.1787 | 0.8333 | 9.1491 | 9.2241    |
| 3.4547        | 15.0  | 750  | 2.6966          | 7.8778 | 0.3333 | 7.8306 | 7.9167    |
| 3.4386        | 16.0  | 800  | 2.6892          | 8.3907 | 0.3333 | 8.3167 | 8.4       |
| 3.3749        | 17.0  | 850  | 2.6786          | 8.6167 | 0.4167 | 8.5917 | 8.5787    |
| 3.3681        | 18.0  | 900  | 2.6895          | 8.2466 | 0.4167 | 8.1799 | 8.2407    |
| 3.3173        | 19.0  | 950  | 2.6957          | 8.1742 | 0.4167 | 8.1197 | 8.1429    |
| 3.3034        | 20.0  | 1000 | 2.6721          | 8.2466 | 0.4167 | 8.1799 | 8.2407    |
| 3.2594        | 21.0  | 1050 | 2.6698          | 8.569  | 0.4167 | 8.5419 | 8.619     |
| 3.2138        | 22.0  | 1100 | 2.6676          | 8.2722 | 0.4167 | 8.2343 | 8.3037    |
| 3.2239        | 23.0  | 1150 | 2.6537          | 8.1444 | 0.4167 | 8.1051 | 8.1301    |
| 3.1887        | 24.0  | 1200 | 2.6529          | 8.1444 | 0.4167 | 8.1051 | 8.1301    |
| 3.1641        | 25.0  | 1250 | 2.6685          | 7.7777 | 0.1667 | 7.7204 | 7.8143    |
| 3.162         | 26.0  | 1300 | 2.6619          | 8.3776 | 0.3333 | 8.4135 | 8.4692    |
| 3.1114        | 27.0  | 1350 | 2.6632          | 8.3776 | 0.3333 | 8.4135 | 8.4692    |
| 3.0645        | 28.0  | 1400 | 2.6438          | 7.8811 | 0.3333 | 7.8333 | 7.9484    |
| 3.0984        | 29.0  | 1450 | 2.6384          | 7.3936 | 0.1667 | 7.3609 | 7.4051    |
| 3.0712        | 30.0  | 1500 | 2.6389          | 6.9609 | 0.1667 | 6.875  | 7.0253    |
| 3.0662        | 31.0  | 1550 | 2.6346          | 7.95   | 0.1667 | 7.9051 | 8.0218    |
| 3.0294        | 32.0  | 1600 | 2.6420          | 7.3936 | 0.1667 | 7.3609 | 7.4051    |
| 3.0143        | 33.0  | 1650 | 2.6325          | 7.6526 | 0.1667 | 7.6869 | 7.7551    |
| 3.002         | 34.0  | 1700 | 2.6384          | 7.9436 | 0.1667 | 7.9317 | 8.016     |
| 2.9964        | 35.0  | 1750 | 2.6262          | 8.2958 | 0.4167 | 8.2317 | 8.3936    |
| 2.9893        | 36.0  | 1800 | 2.6351          | 8.6535 | 0.1667 | 8.616  | 8.7333    |
| 2.9862        | 37.0  | 1850 | 2.6320          | 8.2452 | 0.1667 | 8.2    | 8.3218    |
| 2.9588        | 38.0  | 1900 | 2.6214          | 7.6656 | 0.1667 | 7.6819 | 7.7       |
| 2.9697        | 39.0  | 1950 | 2.6229          | 7.1452 | 0.1667 | 7.1051 | 7.1942    |
| 2.9433        | 40.0  | 2000 | 2.6209          | 7.5775 | 0.4167 | 7.4893 | 7.5833    |
| 2.9306        | 41.0  | 2050 | 2.6197          | 7.525  | 0.4167 | 7.4435 | 7.5351    |
| 2.9382        | 42.0  | 2100 | 2.6190          | 7.525  | 0.4167 | 7.4435 | 7.5351    |
| 2.9269        | 43.0  | 2150 | 2.6234          | 7.3614 | 0.4167 | 7.2092 | 7.3592    |
| 2.9152        | 44.0  | 2200 | 2.6237          | 6.9976 | 0.1667 | 6.8777 | 7.0333    |
| 2.9137        | 45.0  | 2250 | 2.6213          | 6.9976 | 0.1667 | 6.8777 | 7.0333    |
| 2.9011        | 46.0  | 2300 | 2.6212          | 6.9976 | 0.1667 | 6.8777 | 7.0333    |
| 2.8941        | 47.0  | 2350 | 2.6188          | 6.7768 | 0.1667 | 6.6509 | 6.812     |
| 2.9143        | 48.0  | 2400 | 2.6126          | 7.0875 | 0.1667 | 6.803  | 6.9337    |
| 2.8798        | 49.0  | 2450 | 2.6207          | 6.4458 | 0.1667 | 6.3221 | 6.4527    |
| 2.8701        | 50.0  | 2500 | 2.6172          | 6.7542 | 0.1667 | 6.4857 | 6.5729    |
| 2.8823        | 51.0  | 2550 | 2.6161          | 6.9971 | 0.1667 | 6.6819 | 6.7968    |
| 2.8724        | 52.0  | 2600 | 2.6171          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |
| 2.8635        | 53.0  | 2650 | 2.6176          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |
| 2.8803        | 54.0  | 2700 | 2.6134          | 6.1417 | 0.1667 | 5.929  | 6.0423    |
| 2.8608        | 55.0  | 2750 | 2.6118          | 6.4953 | 0.1667 | 6.2113 | 6.3554    |
| 2.8655        | 56.0  | 2800 | 2.6125          | 6.4976 | 0.1667 | 6.2625 | 6.3539    |
| 2.856         | 57.0  | 2850 | 2.6136          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |
| 2.8837        | 58.0  | 2900 | 2.6124          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |
| 2.8871        | 59.0  | 2950 | 2.6123          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |
| 2.8537        | 60.0  | 3000 | 2.6123          | 6.8298 | 0.1667 | 6.5947 | 6.6685    |


### 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-7", "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-7
===========================


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: 2.6123
* Rouge1: 6.8298
* Rouge2: 0.1667
* Rougel: 6.5947
* Rougelsum: 6.6685


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