
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# t5_small_SA_abbr_replaced

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:
- Loss: 0.5633
- Rouge1: 0.2325
- Rouge2: 0.1112
- Rougel: 0.2228
- Rougelsum: 0.2228
- Gen Len: 13.115
- Bert Score F1: 0.8407
- Bert Score Precision: 0.8551
- Bert Score Recall: 0.8282

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert Score F1 | Bert Score Precision | Bert Score Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:-------------:|:--------------------:|:-----------------:|
| 0.6839        | 0.95  | 500  | 0.6228          | 0.1495 | 0.0654 | 0.1433 | 0.1421    | 10.3894 | 0.6689        | 0.6835               | 0.656             |
| 0.5918        | 1.9   | 1000 | 0.6084          | 0.1965 | 0.0775 | 0.1893 | 0.1904    | 13.6991 | 0.8138        | 0.827                | 0.8024            |
| 0.5917        | 2.85  | 1500 | 0.5943          | 0.2075 | 0.0804 | 0.1975 | 0.1982    | 12.5841 | 0.8293        | 0.8452               | 0.8153            |
| 0.5524        | 3.8   | 2000 | 0.5859          | 0.2104 | 0.0856 | 0.1994 | 0.2       | 13.4602 | 0.8373        | 0.8523               | 0.8242            |
| 0.5635        | 4.74  | 2500 | 0.5830          | 0.2069 | 0.0905 | 0.1974 | 0.1984    | 12.1327 | 0.8379        | 0.8549               | 0.8231            |
| 0.5455        | 5.69  | 3000 | 0.5756          | 0.2113 | 0.0963 | 0.2038 | 0.2032    | 12.6903 | 0.8398        | 0.8545               | 0.8269            |
| 0.5156        | 6.64  | 3500 | 0.5710          | 0.2147 | 0.0993 | 0.2093 | 0.2096    | 13.115  | 0.8393        | 0.8527               | 0.8278            |
| 0.5134        | 7.59  | 4000 | 0.5683          | 0.2252 | 0.105  | 0.216  | 0.2168    | 12.4513 | 0.8418        | 0.8572               | 0.8285            |
| 0.5381        | 8.54  | 4500 | 0.5661          | 0.2228 | 0.1051 | 0.215  | 0.2151    | 13.4602 | 0.8401        | 0.8534               | 0.8287            |
| 0.5092        | 9.49  | 5000 | 0.5643          | 0.2186 | 0.1027 | 0.2135 | 0.2132    | 13.1593 | 0.8385        | 0.8524               | 0.8265            |
| 0.5181        | 10.44 | 5500 | 0.5643          | 0.2299 | 0.1087 | 0.2196 | 0.2202    | 13.0    | 0.8398        | 0.8543               | 0.8273            |
| 0.4824        | 11.39 | 6000 | 0.5633          | 0.2325 | 0.1112 | 0.2228 | 0.2228    | 13.115  | 0.8407        | 0.8551               | 0.8282            |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5_small_SA_abbr_replaced", "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\_SA\_abbr\_replaced
=============================


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


* Loss: 0.5633
* Rouge1: 0.2325
* Rouge2: 0.1112
* Rougel: 0.2228
* Rougelsum: 0.2228
* Gen Len: 13.115
* Bert Score F1: 0.8407
* Bert Score Precision: 0.8551
* Bert Score Recall: 0.8282


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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2