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# kapuska/t5-small-finetuned-on-800-records-samsum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7883
- Validation Loss: 2.3752
- Train Rouge1: 24.8093
- Train Rouge2: 8.8889
- Train Rougel: 22.6817
- Train Rougelsum: 22.6817
- Train Gen Len: 19.0
- Epoch: 99

## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.9252     | 1.9205          | 19.5556      | 2.3256       | 15.1111      | 15.1111         | 19.0          | 0     |
| 1.9005     | 1.9227          | 17.5579      | 2.3810       | 15.2852      | 15.2852         | 19.0          | 1     |
| 1.8769     | 1.9228          | 17.5579      | 2.3810       | 15.2852      | 15.2852         | 19.0          | 2     |
| 1.8463     | 1.9192          | 17.5579      | 2.3810       | 15.2852      | 15.2852         | 19.0          | 3     |
| 1.8251     | 1.9132          | 17.4786      | 2.3256       | 13.0342      | 13.0342         | 19.0          | 4     |
| 1.8148     | 1.9147          | 15.5594      | 2.3810       | 13.2867      | 13.2867         | 19.0          | 5     |
| 1.7980     | 1.9142          | 15.5594      | 2.3810       | 13.2867      | 13.2867         | 19.0          | 6     |
| 1.7684     | 1.9158          | 15.6772      | 2.3810       | 13.4045      | 13.4045         | 19.0          | 7     |
| 1.7571     | 1.9161          | 17.5964      | 2.3256       | 13.1519      | 13.1519         | 19.0          | 8     |
| 1.7345     | 1.9221          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 9     |
| 1.7136     | 1.9141          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 10    |
| 1.6935     | 1.9249          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 11    |
| 1.6685     | 1.9226          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 12    |
| 1.6571     | 1.9258          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 13    |
| 1.6327     | 1.9308          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 14    |
| 1.6295     | 1.9271          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 15    |
| 1.6112     | 1.9314          | 19.5556      | 2.3256       | 15.1111      | 15.1111         | 19.0          | 16    |
| 1.6008     | 1.9357          | 19.6372      | 2.3256       | 15.1927      | 15.1927         | 19.0          | 17    |
| 1.5826     | 1.9277          | 19.3913      | 2.2727       | 15.0435      | 15.0435         | 19.0          | 18    |
| 1.5784     | 1.9342          | 21.3913      | 2.2727       | 17.0435      | 17.0435         | 19.0          | 19    |
| 1.5553     | 1.9364          | 19.3913      | 2.2727       | 15.0435      | 15.0435         | 19.0          | 20    |
| 1.5292     | 1.9461          | 19.3913      | 2.2727       | 15.0435      | 15.0435         | 19.0          | 21    |
| 1.5114     | 1.9505          | 19.3913      | 2.2727       | 15.0435      | 15.0435         | 19.0          | 22    |
| 1.5042     | 1.9540          | 17.5964      | 2.3256       | 13.1519      | 13.1519         | 19.0          | 23    |
| 1.4964     | 1.9494          | 19.0621      | 4.4444       | 16.9344      | 16.9344         | 19.0          | 24    |
| 1.4736     | 1.9569          | 24.7136      | 4.4444       | 20.6628      | 22.5859         | 19.0          | 25    |
| 1.4644     | 1.9618          | 24.7136      | 4.4444       | 20.6628      | 22.5859         | 19.0          | 26    |
| 1.4562     | 1.9693          | 18.9821      | 4.4444       | 16.8544      | 16.8544         | 19.0          | 27    |
| 1.4339     | 1.9597          | 22.7905      | 4.4444       | 18.7398      | 20.6628         | 19.0          | 28    |
| 1.4204     | 1.9702          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 29    |
| 1.4182     | 1.9715          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 30    |
| 1.4014     | 1.9768          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 31    |
| 1.3845     | 1.9847          | 20.9428      | 4.4444       | 18.8152      | 18.8152         | 19.0          | 32    |
| 1.3756     | 1.9790          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 33    |
| 1.3611     | 1.9936          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 34    |
| 1.3495     | 1.9900          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 35    |
| 1.3403     | 1.9998          | 20.9428      | 4.4444       | 18.8152      | 18.8152         | 19.0          | 36    |
| 1.3253     | 2.0060          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 37    |
| 1.3109     | 2.0088          | 18.9821      | 4.4444       | 16.8544      | 16.8544         | 19.0          | 38    |
| 1.3106     | 2.0121          | 20.8674      | 4.4444       | 18.7398      | 18.7398         | 19.0          | 39    |
| 1.2903     | 2.0142          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 40    |
| 1.2795     | 2.0239          | 20.8674      | 4.4444       | 18.7398      | 18.7398         | 19.0          | 41    |
| 1.2788     | 2.0322          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 42    |
| 1.2629     | 2.0284          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 43    |
| 1.2525     | 2.0423          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 44    |
| 1.2373     | 2.0424          | 27.0458      | 11.1111      | 22.9951      | 24.9182         | 19.0          | 45    |
| 1.2242     | 2.0454          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 46    |
| 1.2214     | 2.0541          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 47    |
| 1.2066     | 2.0567          | 27.0458      | 11.1111      | 22.9951      | 24.9182         | 19.0          | 48    |
| 1.1866     | 2.0632          | 26.9370      | 11.1111      | 24.8093      | 24.8093         | 19.0          | 49    |
| 1.1976     | 2.0684          | 27.0458      | 11.1111      | 22.9951      | 24.9182         | 19.0          | 50    |
| 1.1806     | 2.0725          | 27.0458      | 11.1111      | 22.9951      | 24.9182         | 19.0          | 51    |
| 1.1662     | 2.0803          | 27.0458      | 11.1111      | 22.9951      | 24.9182         | 19.0          | 52    |
| 1.1626     | 2.0840          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 53    |
| 1.1464     | 2.0855          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 54    |
| 1.1298     | 2.0956          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 55    |
| 1.1300     | 2.1050          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 56    |
| 1.1255     | 2.1025          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 57    |
| 1.1005     | 2.1188          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 58    |
| 1.1002     | 2.1261          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 59    |
| 1.0806     | 2.1318          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 60    |
| 1.0869     | 2.1425          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 61    |
| 1.0768     | 2.1492          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 62    |
| 1.0681     | 2.1473          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 63    |
| 1.0594     | 2.1440          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 64    |
| 1.0411     | 2.1461          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 65    |
| 1.0342     | 2.1727          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 66    |
| 1.0306     | 2.1677          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 67    |
| 1.0163     | 2.1753          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 68    |
| 1.0139     | 2.1767          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 69    |
| 1.0036     | 2.1929          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 70    |
| 1.0049     | 2.1902          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 71    |
| 0.9947     | 2.1936          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 72    |
| 0.9803     | 2.2084          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 73    |
| 0.9791     | 2.2106          | 19.3144      | 4.5455       | 17.1405      | 17.1405         | 19.0          | 74    |
| 0.9655     | 2.2172          | 20.8674      | 4.4444       | 18.7398      | 18.7398         | 19.0          | 75    |
| 0.9640     | 2.2215          | 22.6817      | 4.4444       | 20.5540      | 20.5540         | 19.0          | 76    |
| 0.9456     | 2.2341          | 26.9370      | 11.1111      | 24.8093      | 24.8093         | 19.0          | 77    |
| 0.9396     | 2.2414          | 23.0705      | 8.8889       | 20.9428      | 20.9428         | 19.0          | 78    |
| 0.9335     | 2.2455          | 18.9444      | 4.4444       | 16.8167      | 16.8167         | 19.0          | 79    |
| 0.9261     | 2.2560          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 80    |
| 0.9075     | 2.2642          | 23.1997      | 11.1111      | 21.0720      | 21.0720         | 19.0          | 81    |
| 0.9023     | 2.2763          | 22.9951      | 8.8889       | 20.8674      | 20.8674         | 19.0          | 82    |
| 0.9044     | 2.2782          | 21.0720      | 8.8889       | 18.9444      | 18.9444         | 19.0          | 83    |
| 0.8961     | 2.2812          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 84    |
| 0.8813     | 2.2794          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 85    |
| 0.8731     | 2.2886          | 21.0720      | 8.8889       | 18.9444      | 18.9444         | 19.0          | 86    |
| 0.8751     | 2.2930          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 87    |
| 0.8652     | 2.3024          | 25.2256      | 6.8182       | 23.0517      | 23.0517         | 19.0          | 88    |
| 0.8605     | 2.3131          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 89    |
| 0.8571     | 2.3070          | 22.9951      | 8.8889       | 20.8674      | 20.8674         | 19.0          | 90    |
| 0.8473     | 2.3123          | 25.1227      | 11.1111      | 22.9951      | 22.9951         | 19.0          | 91    |
| 0.8456     | 2.3272          | 25.1227      | 11.1111      | 22.9951      | 22.9951         | 19.0          | 92    |
| 0.8329     | 2.3427          | 26.9370      | 11.1111      | 24.8093      | 24.8093         | 19.0          | 93    |
| 0.8294     | 2.3419          | 25.1982      | 11.1111      | 23.0705      | 23.0705         | 19.0          | 94    |
| 0.8243     | 2.3507          | 25.1982      | 11.1111      | 23.0705      | 23.0705         | 19.0          | 95    |
| 0.8132     | 2.3600          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 96    |
| 0.8153     | 2.3501          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 97    |
| 0.8005     | 2.3579          | 20.8778      | 2.2727       | 18.7039      | 18.7039         | 19.0          | 98    |
| 0.7883     | 2.3752          | 24.8093      | 8.8889       | 22.6817      | 22.6817         | 19.0          | 99    |


### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "kapuska/t5-small-finetuned-on-800-records-samsum", "results": []}]}
transformers tf tensorboard t5 text2text-generation generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

kapuska/t5-small-finetuned-on-800-records-samsum
================================================


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


* Train Loss: 0.7883
* Validation Loss: 2.3752
* Train Rouge1: 24.8093
* Train Rouge2: 8.8889
* Train Rougel: 22.6817
* Train Rougelsum: 22.6817
* Train Gen Len: 19.0
* Epoch: 99


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:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1