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# t5-base-finetuned-xsum

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1894
- Rouge1: 34.6512
- Rouge2: 19.303
- Rougel: 32.5996
- Rougelsum: 32.471
- Gen Len: 7.0

## 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: 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 13   | 2.5904          | 31.7728 | 17.9506 | 31.6663 | 32.1172   | 7.2609  |
| No log        | 2.0   | 26   | 2.5238          | 33.4485 | 17.2356 | 33.0828 | 33.5748   | 7.8696  |
| No log        | 3.0   | 39   | 2.4739          | 31.3343 | 15.996  | 31.2341 | 31.646    | 9.0435  |
| No log        | 4.0   | 52   | 2.4413          | 31.2481 | 16.395  | 31.2318 | 31.633    | 8.0435  |
| No log        | 5.0   | 65   | 2.4156          | 28.9275 | 13.9479 | 28.7067 | 29.3501   | 8.2609  |
| No log        | 6.0   | 78   | 2.3768          | 25.1989 | 12.2726 | 25.1301 | 25.331    | 9.6087  |
| No log        | 7.0   | 91   | 2.3599          | 25.7646 | 12.6915 | 25.745  | 26.2307   | 9.0     |
| No log        | 8.0   | 104  | 2.3460          | 27.3509 | 12.7398 | 27.3198 | 27.7394   | 8.3043  |
| No log        | 9.0   | 117  | 2.3134          | 26.4175 | 12.3464 | 26.3116 | 26.6575   | 8.2609  |
| No log        | 10.0  | 130  | 2.3089          | 25.4818 | 12.381  | 25.4929 | 25.9985   | 7.913   |
| No log        | 11.0  | 143  | 2.2976          | 27.5605 | 13.3195 | 27.4523 | 27.7723   | 7.0     |
| No log        | 12.0  | 156  | 2.2951          | 29.0566 | 13.3195 | 29.1363 | 29.4032   | 7.0     |
| No log        | 13.0  | 169  | 2.2770          | 27.4586 | 13.2436 | 27.3583 | 27.7153   | 7.1304  |
| No log        | 14.0  | 182  | 2.2675          | 27.4586 | 13.2436 | 27.3583 | 27.7153   | 7.1304  |
| No log        | 15.0  | 195  | 2.2573          | 27.4586 | 13.2436 | 27.3583 | 27.7153   | 7.0     |
| No log        | 16.0  | 208  | 2.2523          | 28.0801 | 13.2436 | 28.0208 | 28.4869   | 6.6957  |
| No log        | 17.0  | 221  | 2.2410          | 29.7194 | 13.9268 | 29.6547 | 29.9577   | 6.7826  |
| No log        | 18.0  | 234  | 2.2355          | 29.8201 | 13.9268 | 29.7716 | 30.1114   | 7.0     |
| No log        | 19.0  | 247  | 2.2350          | 28.4851 | 13.6025 | 28.3633 | 28.6581   | 7.0435  |
| No log        | 20.0  | 260  | 2.2219          | 26.4416 | 12.7674 | 25.9806 | 26.2749   | 6.6522  |
| No log        | 21.0  | 273  | 2.2036          | 26.6032 | 13.0124 | 26.25   | 26.4627   | 6.8261  |
| No log        | 22.0  | 286  | 2.1974          | 27.4656 | 13.0124 | 27.2275 | 27.2769   | 6.2174  |
| No log        | 23.0  | 299  | 2.1962          | 26.4416 | 12.7674 | 25.9806 | 26.2749   | 6.6957  |
| No log        | 24.0  | 312  | 2.2099          | 26.1686 | 12.6018 | 25.7118 | 25.9094   | 6.7391  |
| No log        | 25.0  | 325  | 2.1990          | 27.3084 | 12.7709 | 27.0236 | 27.0354   | 6.1739  |
| No log        | 26.0  | 338  | 2.1942          | 29.0825 | 12.7709 | 28.6736 | 29.3337   | 6.3478  |
| No log        | 27.0  | 351  | 2.2058          | 29.0825 | 12.7709 | 27.5837 | 28.1575   | 6.0     |
| No log        | 28.0  | 364  | 2.2012          | 29.0825 | 12.7709 | 27.5837 | 28.1575   | 6.0     |
| No log        | 29.0  | 377  | 2.1992          | 28.1338 | 12.7709 | 26.6664 | 27.0659   | 6.8696  |
| No log        | 30.0  | 390  | 2.1840          | 32.5399 | 17.1325 | 31.2654 | 31.4223   | 6.6087  |
| No log        | 31.0  | 403  | 2.1824          | 32.5399 | 17.1325 | 31.2654 | 31.4223   | 6.7826  |
| No log        | 32.0  | 416  | 2.1830          | 34.6512 | 19.303  | 33.1484 | 33.1392   | 6.8261  |
| No log        | 33.0  | 429  | 2.1846          | 33.0599 | 19.303  | 31.3736 | 31.5588   | 6.6522  |
| No log        | 34.0  | 442  | 2.1868          | 33.0599 | 19.303  | 31.3736 | 31.5588   | 6.6522  |
| No log        | 35.0  | 455  | 2.1803          | 35.5538 | 19.303  | 34.135  | 34.0635   | 6.087   |
| No log        | 36.0  | 468  | 2.1779          | 35.5538 | 19.303  | 33.5533 | 33.5085   | 6.087   |
| No log        | 37.0  | 481  | 2.1770          | 34.9683 | 19.303  | 33.2356 | 33.1109   | 6.1739  |
| No log        | 38.0  | 494  | 2.1845          | 35.5538 | 19.303  | 33.5533 | 33.5085   | 6.3478  |
| 1.8275        | 39.0  | 507  | 2.1867          | 34.6512 | 19.303  | 32.5996 | 32.471    | 7.0     |
| 1.8275        | 40.0  | 520  | 2.1881          | 36.4717 | 19.7895 | 34.9234 | 34.7549   | 6.913   |
| 1.8275        | 41.0  | 533  | 2.1877          | 36.4717 | 19.7895 | 34.9234 | 34.7549   | 6.913   |
| 1.8275        | 42.0  | 546  | 2.1842          | 36.4717 | 19.7895 | 34.9234 | 34.7549   | 6.913   |
| 1.8275        | 43.0  | 559  | 2.1869          | 36.4717 | 19.7895 | 34.3175 | 34.1247   | 6.913   |
| 1.8275        | 44.0  | 572  | 2.1914          | 36.4717 | 19.7895 | 34.3175 | 34.1247   | 6.913   |
| 1.8275        | 45.0  | 585  | 2.1921          | 36.4717 | 19.7895 | 34.3175 | 34.1247   | 6.913   |
| 1.8275        | 46.0  | 598  | 2.1910          | 36.4717 | 19.7895 | 34.3175 | 34.1247   | 6.913   |
| 1.8275        | 47.0  | 611  | 2.1903          | 34.6512 | 19.303  | 32.5996 | 32.471    | 7.0     |
| 1.8275        | 48.0  | 624  | 2.1904          | 34.6512 | 19.303  | 32.5996 | 32.471    | 7.0     |
| 1.8275        | 49.0  | 637  | 2.1896          | 34.6512 | 19.303  | 32.5996 | 32.471    | 7.0     |
| 1.8275        | 50.0  | 650  | 2.1894          | 34.6512 | 19.303  | 32.5996 | 32.471    | 7.0     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-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-base-finetuned-xsum
======================


This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1894
* Rouge1: 34.6512
* Rouge2: 19.303
* Rougel: 32.5996
* Rougelsum: 32.471
* Gen Len: 7.0


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: 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: 50


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.0
* 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: 2e-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: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.0
* Tokenizers 0.13.3