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# cnn_dailymail-summarization-t5-small-2022-09-05

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6455
- Rouge1: 41.4235
- Rouge2: 19.0263
- Rougel: 29.2892
- Rougelsum: 38.6338
- Gen Len: 73.7273

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step   | Gen Len | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.9623        | 0.03  | 1000   | 18.9996 | 1.7500          | 24.1039 | 11.368  | 19.813  | 22.671    |
| 1.8827        | 0.06  | 2000   | 18.9993 | 1.7382          | 24.1445 | 11.4497 | 19.8683 | 22.7376   |
| 1.8988        | 0.08  | 3000   | 18.9999 | 1.7310          | 24.329  | 11.5899 | 20.0409 | 22.9104   |
| 1.8778        | 0.11  | 4000   | 19.0    | 1.7177          | 24.3886 | 11.6472 | 20.1048 | 22.988    |
| 1.9173        | 0.14  | 5000   | 18.9996 | 1.7140          | 24.3508 | 11.5594 | 20.075  | 22.932    |
| 1.9009        | 0.17  | 6000   | 18.9995 | 1.7134          | 24.28   | 11.6075 | 20.0581 | 22.8833   |
| 1.8975        | 0.2   | 7000   | 18.9994 | 1.7081          | 24.3203 | 11.6175 | 20.035  | 22.9167   |
| 1.8835        | 0.22  | 8000   | 18.9996 | 1.7061          | 24.2729 | 11.6324 | 20.0728 | 22.8747   |
| 1.8725        | 0.25  | 9000   | 19.0    | 1.6995          | 24.2542 | 11.5763 | 20.0241 | 22.8713   |
| 1.837         | 0.28  | 10000  | 18.9997 | 1.6998          | 24.3321 | 11.599  | 20.1028 | 22.9562   |
| 1.8629        | 0.31  | 11000  | 19.0    | 1.6944          | 24.4161 | 11.6208 | 20.1374 | 23.024    |
| 1.85          | 0.33  | 12000  | 18.9990 | 1.7002          | 24.3514 | 11.6883 | 20.134  | 22.9515   |
| 1.8506        | 0.36  | 13000  | 18.9987 | 1.6894          | 24.3812 | 11.6592 | 20.1641 | 23.0108   |
| 1.8869        | 0.39  | 14000  | 18.9995 | 1.6881          | 24.3956 | 11.6817 | 20.1654 | 23.0284   |
| 1.8327        | 0.42  | 15000  | 18.9993 | 1.6903          | 24.3707 | 11.6446 | 20.1353 | 22.9801   |
| 1.8204        | 0.45  | 16000  | 18.9993 | 1.6896          | 24.3663 | 11.6963 | 20.1357 | 22.9898   |
| 1.8764        | 0.47  | 17000  | 18.9978 | 1.6846          | 24.4212 | 11.652  | 20.1455 | 23.0326   |
| 1.8213        | 0.5   | 18000  | 18.9992 | 1.6817          | 24.452  | 11.7014 | 20.1898 | 23.0668   |
| 1.8424        | 0.53  | 19000  | 18.9990 | 1.6844          | 24.4206 | 11.7049 | 20.1931 | 23.0358   |
| 1.8721        | 0.56  | 20000  | 18.9996 | 1.6814          | 24.4483 | 11.6789 | 20.1798 | 23.0508   |
| 1.87          | 0.59  | 21000  | 18.9996 | 1.6796          | 24.4799 | 11.6789 | 20.1919 | 23.0831   |
| 1.844         | 0.61  | 22000  | 18.9996 | 1.6770          | 24.4741 | 11.7433 | 20.2031 | 23.0535   |
| 1.8611        | 0.64  | 23000  | 18.9986 | 1.6785          | 24.4837 | 11.7572 | 20.219  | 23.088    |
| 1.8201        | 0.67  | 24000  | 18.9993 | 1.6796          | 24.3955 | 11.6978 | 20.173  | 23.0302   |
| 1.8506        | 0.7   | 25000  | 18.9995 | 1.6770          | 24.4084 | 11.711  | 20.1851 | 23.0266   |
| 1.846         | 0.72  | 26000  | 18.9990 | 1.6765          | 24.4272 | 11.6779 | 20.1785 | 23.0352   |
| 1.8431        | 0.75  | 27000  | 18.9998 | 1.6757          | 24.4484 | 11.7154 | 20.2156 | 23.0646   |
| 1.8208        | 0.78  | 28000  | 18.9993 | 1.6764          | 24.412  | 11.6887 | 20.1752 | 23.0151   |
| 1.8108        | 0.81  | 29000  | 18.9997 | 1.6733          | 24.4051 | 11.7155 | 20.1773 | 23.0215   |
| 1.847         | 0.84  | 30000  | 18.9994 | 1.6738          | 24.5531 | 11.7949 | 20.2834 | 23.1588   |
| 1.8386        | 0.86  | 31000  | 18.9991 | 1.6674          | 24.5155 | 11.7333 | 20.2529 | 23.145    |
| 1.82          | 0.89  | 32000  | 18.9988 | 1.6693          | 24.4498 | 11.7118 | 20.2183 | 23.0767   |
| 1.8475        | 0.92  | 33000  | 18.9993 | 1.6676          | 24.442  | 11.676  | 20.168  | 23.0409   |
| 1.7948        | 0.95  | 34000  | 18.9990 | 1.6689          | 24.4561 | 11.7865 | 20.2446 | 23.0707   |
| 1.8357        | 0.98  | 35000  | 18.9994 | 1.6757          | 24.4005 | 11.7299 | 20.1999 | 23.0093   |
| 1.8624        | 1.0   | 36000  | 18.9988 | 1.6745          | 24.3371 | 11.6749 | 20.1257 | 22.9428   |
| 1.8309        | 1.03  | 37000  | 18.9995 | 1.6675          | 24.5108 | 11.8038 | 20.2691 | 23.117    |
| 1.8237        | 1.06  | 38000  | 18.9996 | 1.6654          | 24.482  | 11.7485 | 20.2225 | 23.0917   |
| 1.7743        | 1.09  | 39000  | 18.9993 | 1.6681          | 24.5106 | 11.7511 | 20.2583 | 23.123    |
| 1.7811        | 1.11  | 40000  | 18.9991 | 1.6636          | 24.6194 | 11.843  | 20.3375 | 23.2259   |
| 1.7973        | 1.14  | 41000  | 19.0    | 1.6666          | 24.5434 | 11.8133 | 20.3033 | 23.165    |
| 1.8156        | 1.17  | 42000  | 18.9993 | 1.6660          | 24.4857 | 11.7526 | 20.2406 | 23.1081   |
| 1.8403        | 1.2   | 43000  | 18.9998 | 1.6621          | 24.4632 | 11.7525 | 20.2459 | 23.0692   |
| 1.8129        | 1.23  | 44000  | 18.9999 | 1.6643          | 24.6032 | 11.8251 | 20.3368 | 23.1806   |
| 1.7896        | 1.25  | 45000  | 18.9993 | 1.6622          | 24.4619 | 11.7769 | 20.2516 | 23.0647   |
| 1.7948        | 1.28  | 46000  | 18.9992 | 1.6608          | 24.5468 | 11.8041 | 20.2941 | 23.1551   |
| 1.8043        | 1.31  | 47000  | 18.9993 | 1.6614          | 24.5774 | 11.8246 | 20.3189 | 23.1836   |
| 1.7884        | 1.34  | 48000  | 18.9993 | 1.6581          | 24.5688 | 11.843  | 20.2993 | 23.1756   |
| 1.8041        | 1.37  | 49000  | 18.9996 | 1.6614          | 24.5454 | 11.8346 | 20.3179 | 23.1605   |
| 1.8192        | 1.39  | 50000  | 18.9998 | 1.6597          | 24.5017 | 11.7755 | 20.2439 | 23.1148   |
| 1.8679        | 1.42  | 51000  | 18.9995 | 1.6555          | 24.5302 | 11.7638 | 20.2592 | 23.1395   |
| 1.82          | 1.45  | 52000  | 18.9998 | 1.6571          | 24.546  | 11.7798 | 20.265  | 23.1408   |
| 1.8267        | 1.48  | 53000  | 18.9996 | 1.6552          | 24.5214 | 11.7368 | 20.2276 | 23.1504   |
| 1.8063        | 1.5   | 54000  | 18.9992 | 1.6588          | 24.5222 | 11.8209 | 20.2941 | 23.1551   |
| 1.8171        | 1.53  | 55000  | 18.9996 | 1.6569          | 24.5845 | 11.8182 | 20.3147 | 23.1812   |
| 1.7884        | 1.56  | 56000  | 18.9998 | 1.6597          | 24.532  | 11.8057 | 20.2622 | 23.1459   |
| 1.7588        | 1.59  | 57000  | 18.9994 | 1.6572          | 24.6532 | 11.8958 | 20.3877 | 23.2776   |
| 1.7847        | 1.62  | 58000  | 18.9996 | 1.6561          | 24.5483 | 11.856  | 20.3188 | 23.1852   |
| 1.8523        | 1.64  | 59000  | 18.9996 | 1.6584          | 24.5501 | 11.8666 | 20.3197 | 23.1683   |
| 1.7955        | 1.67  | 60000  | 18.9999 | 1.6546          | 24.5126 | 11.8043 | 20.2603 | 23.1175   |
| 1.8215        | 1.7   | 61000  | 18.9996 | 1.6541          | 24.5884 | 11.8003 | 20.2887 | 23.1866   |
| 1.7917        | 1.73  | 62000  | 18.9997 | 1.6568          | 24.619  | 11.8868 | 20.3496 | 23.2304   |
| 1.7543        | 1.76  | 63000  | 18.9996 | 1.6570          | 24.5378 | 11.8192 | 20.2681 | 23.1454   |
| 1.7978        | 1.78  | 64000  | 18.9999 | 1.6541          | 24.5719 | 11.8446 | 20.2873 | 23.1855   |
| 1.8228        | 1.81  | 65000  | 18.9998 | 1.6561          | 24.5193 | 11.8527 | 20.3185 | 23.1395   |
| 1.8163        | 1.84  | 66000  | 18.9998 | 1.6537          | 24.4385 | 11.7625 | 20.2042 | 23.0671   |
| 1.7868        | 1.87  | 67000  | 18.9998 | 1.6532          | 24.4985 | 11.8187 | 20.2775 | 23.1426   |
| 1.8345        | 1.89  | 68000  | 18.9999 | 1.6522          | 24.5375 | 11.8398 | 20.285  | 23.1643   |
| 1.7773        | 1.92  | 69000  | 18.9999 | 1.6529          | 24.4722 | 11.7979 | 20.2636 | 23.106    |
| 1.8409        | 1.95  | 70000  | 18.9999 | 1.6521          | 24.4845 | 11.8136 | 20.2557 | 23.1089   |
| 1.8146        | 1.98  | 71000  | 18.9999 | 1.6515          | 24.4923 | 11.7965 | 20.2521 | 23.1247   |
| 1.7466        | 2.01  | 72000  | 19.0    | 1.6526          | 24.4913 | 11.8254 | 20.2562 | 23.1266   |
| 1.8009        | 2.03  | 73000  | 19.0    | 1.6505          | 24.5231 | 11.8414 | 20.2842 | 23.1654   |
| 1.7768        | 2.06  | 74000  | 19.0    | 1.6516          | 24.5192 | 11.8206 | 20.2884 | 23.1493   |
| 1.7569        | 2.09  | 75000  | 19.0    | 1.6541          | 24.6135 | 11.9135 | 20.3513 | 23.2279   |
| 1.7893        | 2.12  | 76000  | 18.9997 | 1.6507          | 24.5934 | 11.8727 | 20.3305 | 23.2106   |
| 1.763         | 2.15  | 77000  | 18.9999 | 1.6512          | 24.5829 | 11.8543 | 20.3142 | 23.2049   |
| 1.7552        | 2.17  | 78000  | 18.9998 | 1.6506          | 24.5332 | 11.8309 | 20.2795 | 23.1654   |
| 1.7632        | 2.2   | 79000  | 18.9995 | 1.6498          | 24.5569 | 11.8313 | 20.3158 | 23.1808   |
| 1.8056        | 2.23  | 80000  | 18.9996 | 1.6488          | 24.6217 | 11.8877 | 20.3555 | 23.2514   |
| 1.8066        | 2.26  | 81000  | 18.9996 | 1.6494          | 24.5799 | 11.8515 | 20.3307 | 23.2059   |
| 1.7903        | 2.28  | 82000  | 18.9998 | 1.6487          | 24.6151 | 11.889  | 20.3739 | 23.2226   |
| 1.805         | 2.31  | 83000  | 18.9996 | 1.6493          | 24.5739 | 11.8659 | 20.3354 | 23.1884   |
| 1.7843        | 2.34  | 84000  | 18.9996 | 1.6487          | 24.6125 | 11.8879 | 20.3648 | 23.2274   |
| 1.8153        | 2.37  | 85000  | 18.9996 | 1.6493          | 24.5638 | 11.8392 | 20.3084 | 23.165    |
| 1.7581        | 2.4   | 86000  | 18.9996 | 1.6490          | 24.6121 | 11.8876 | 20.36   | 23.2163   |
| 1.6925        | 2.42  | 87000  | 18.9998 | 1.6502          | 24.6192 | 11.8992 | 20.3786 | 23.2421   |
| 1.7535        | 2.45  | 88000  | 18.9996 | 1.6473          | 24.6134 | 11.8877 | 20.3663 | 23.2262   |
| 1.751         | 2.48  | 89000  | 18.9996 | 1.6496          | 24.5728 | 11.8886 | 20.3411 | 23.1906   |
| 1.7577        | 2.51  | 90000  | 18.9996 | 1.6477          | 24.5616 | 11.8489 | 20.3021 | 23.1754   |
| 1.8           | 2.54  | 91000  | 18.9996 | 1.6473          | 24.5614 | 11.8663 | 20.3282 | 23.1868   |
| 1.7859        | 2.56  | 92000  | 18.9998 | 1.6483          | 24.5594 | 11.8426 | 20.3197 | 23.191    |
| 1.7984        | 2.59  | 93000  | 18.9998 | 1.6469          | 24.5732 | 11.8258 | 20.3204 | 23.1958   |
| 1.7943        | 2.62  | 94000  | 18.9996 | 1.6477          | 24.5888 | 11.8602 | 20.3352 | 23.2181   |
| 1.7888        | 2.65  | 95000  | 18.9996 | 1.6472          | 24.5781 | 11.844  | 20.3272 | 23.216    |
| 1.7803        | 2.67  | 96000  | 1.6483  | 24.5454         | 11.8245 | 20.2917 | 23.1727 | 18.9996   |
| 1.8106        | 2.7   | 97000  | 1.6461  | 24.5694         | 11.8344 | 20.3123 | 23.1934 | 18.9996   |
| 1.8713        | 2.73  | 98000  | 1.6454  | 24.5906         | 11.8573 | 20.3447 | 23.2181 | 18.9996   |
| 1.7655        | 2.76  | 99000  | 1.6468  | 24.5709         | 11.8573 | 20.3139 | 23.1994 | 18.9996   |
| 1.7616        | 2.79  | 100000 | 1.6464  | 24.5852         | 11.8531 | 20.3172 | 23.2089 | 18.9998   |
| 1.7581        | 2.81  | 101000 | 1.6468  | 24.5748         | 11.8452 | 20.3043 | 23.1849 | 18.9997   |
| 1.7743        | 2.84  | 102000 | 1.6462  | 24.5665         | 11.8328 | 20.2992 | 23.1896 | 18.9996   |
| 1.78          | 2.87  | 103000 | 1.6458  | 24.5716         | 11.8399 | 20.31   | 23.1943 | 18.9996   |
| 1.8162        | 2.9   | 104000 | 1.6456  | 24.5719         | 11.8358 | 20.3132 | 23.1921 | 18.9996   |
| 1.7862        | 2.93  | 105000 | 1.6462  | 24.5938         | 11.8624 | 20.337  | 23.2131 | 18.9996   |
| 1.7995        | 2.95  | 106000 | 1.6459  | 24.5885         | 11.8606 | 20.3325 | 23.2137 | 18.9996   |
| 1.7559        | 2.98  | 107000 | 1.6454  | 24.593          | 11.861  | 20.3401 | 23.2188 | 18.9996   |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "cnn_dailymail-summarization-t5-small-2022-09-05", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail 3.0.0", "type": "cnn_dailymail", "args": "3.0.0"}, "metrics": [{"type": "rouge", "value": 41.4235, "name": "Rouge1"}]}]}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:cnn_dailymail license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

cnn\_dailymail-summarization-t5-small-2022-09-05
================================================


This model is a fine-tuned version of t5-small on the cnn\_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6455
* Rouge1: 41.4235
* Rouge2: 19.0263
* Rougel: 29.2892
* Rougelsum: 38.6338
* Gen Len: 73.7273


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1