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# xsum_and_billsum_and_samsum_old

This model is a fine-tuned version of [psin/xsum_and_billsum_and_samsum](https://huggingface.co/psin/xsum_and_billsum_and_samsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6572
- Rouge1: 0.0873
- Rouge2: 0.0186
- Rougel: 0.0712
- Rougelsum: 0.0711
- Gen Len: 19.0
- Precision: 0.069
- Recall: -0.2018
- F1: -0.0
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled

## 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: 8
- eval_batch_size: 8
- seed: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall  | F1   | Hashcode                                                           |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:-------:|:----:|:------------------------------------------------------------------:|
| No log        | 1.0   | 42   | 3.7360          | 0.0789 | 0.0152 | 0.0679 | 0.068     | 19.0    | 0.071     | -0.2095 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 2.0   | 84   | 3.6936          | 0.085  | 0.0193 | 0.071  | 0.0712    | 19.0    | 0.0898    | -0.2007 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 3.0   | 126  | 3.6739          | 0.0854 | 0.0202 | 0.0712 | 0.0713    | 19.0    | 0.0973    | -0.1935 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 4.0   | 168  | 3.6643          | 0.0865 | 0.0194 | 0.0709 | 0.0709    | 19.0    | 0.0845    | -0.197  | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 5.0   | 210  | 3.6589          | 0.087  | 0.0186 | 0.0711 | 0.0711    | 19.0    | 0.0698    | -0.2012 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 6.0   | 252  | 3.6572          | 0.0873 | 0.0186 | 0.0712 | 0.0711    | 19.0    | 0.069     | -0.2018 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["rouge", "precision", "recall", "f1"], "model-index": [{"name": "xsum_and_billsum_and_samsum_old", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

xsum\_and\_billsum\_and\_samsum\_old
====================================


This model is a fine-tuned version of psin/xsum\_and\_billsum\_and\_samsum on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.6572
* Rouge1: 0.0873
* Rouge2: 0.0186
* Rougel: 0.0712
* Rougelsum: 0.0711
* Gen Len: 19.0
* Precision: 0.069
* Recall: -0.2018
* F1: -0.0
* Hashcode: roberta-large\_L17\_no-idf\_version=0.3.12(hug\_trans=4.28.1)-rescaled


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: 8
* eval\_batch\_size: 8
* seed: 72
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 72
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 8
* eval\_batch\_size: 8
* seed: 72
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3