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# t5-small-finetuned-xsum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7508
- Rouge1: 80.2235
- Rouge2: 65.3985
- Rougel: 72.688
- Rougelsum: 72.6198
- Gen Len: 22.875

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 12   | 1.6130          | 70.4837 | 52.7814 | 63.3244 | 63.3287   | 20.3281 |
| No log        | 2.0   | 24   | 1.4112          | 72.9304 | 54.9574 | 64.9972 | 65.0999   | 21.125  |
| No log        | 3.0   | 36   | 1.2749          | 73.2378 | 55.3429 | 65.6596 | 65.6389   | 21.4844 |
| No log        | 4.0   | 48   | 1.1673          | 73.2812 | 55.4138 | 65.6078 | 65.606    | 21.4688 |
| No log        | 5.0   | 60   | 1.0788          | 73.4664 | 56.0973 | 66.1987 | 66.2205   | 21.4375 |
| No log        | 6.0   | 72   | 1.0031          | 72.9477 | 55.5372 | 65.7097 | 65.6621   | 21.375  |
| No log        | 7.0   | 84   | 0.9399          | 74.12   | 56.6822 | 66.7108 | 66.7065   | 21.6562 |
| No log        | 8.0   | 96   | 0.8896          | 74.1359 | 56.5629 | 66.9081 | 66.8847   | 21.875  |
| No log        | 9.0   | 108  | 0.8495          | 74.2343 | 57.1158 | 67.0403 | 67.057    | 21.8906 |
| No log        | 10.0  | 120  | 0.8210          | 75.9254 | 59.5339 | 69.0056 | 68.979    | 22.1094 |
| No log        | 11.0  | 132  | 0.8033          | 77.9804 | 62.5401 | 70.9546 | 70.9545   | 22.2344 |
| No log        | 12.0  | 144  | 0.7897          | 78.0975 | 62.6435 | 70.8877 | 70.8848   | 22.2969 |
| No log        | 13.0  | 156  | 0.7794          | 78.5314 | 63.3776 | 71.1166 | 70.9786   | 22.4531 |
| No log        | 14.0  | 168  | 0.7729          | 79.2929 | 64.5595 | 72.1205 | 72.0081   | 22.4375 |
| No log        | 15.0  | 180  | 0.7658          | 79.3893 | 64.524  | 72.1437 | 72.0392   | 22.4844 |
| No log        | 16.0  | 192  | 0.7603          | 79.3798 | 64.6518 | 72.1035 | 71.9902   | 22.7031 |
| No log        | 17.0  | 204  | 0.7568          | 79.7509 | 64.9921 | 72.2505 | 72.1764   | 22.7656 |
| No log        | 18.0  | 216  | 0.7534          | 79.7509 | 64.9921 | 72.2505 | 72.1764   | 22.7656 |
| No log        | 19.0  | 228  | 0.7514          | 80.2235 | 65.3985 | 72.688  | 72.6198   | 22.875  |
| No log        | 20.0  | 240  | 0.7508          | 80.2235 | 65.3985 | 72.688  | 72.6198   | 22.875  |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-xsum
=======================


This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7508
* Rouge1: 80.2235
* Rouge2: 65.3985
* Rougel: 72.688
* Rougelsum: 72.6198
* Gen Len: 22.875


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: 4e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #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: 4e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #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: 4e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3