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# xsum_and_billsum_and_old

This model is a fine-tuned version of [psin/xsum_and_billsum](https://huggingface.co/psin/xsum_and_billsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6673
- Rouge1: 0.078
- Rouge2: 0.0127
- Rougel: 0.0648
- Rougelsum: 0.065
- Gen Len: 19.0
- Precision: 0.0673
- Recall: -0.2134
- 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   | 3.7474          | 0.0694 | 0.0093 | 0.0572 | 0.0575    | 19.0    | 0.0475    | -0.2208 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 2.0   | 84   | 3.7070          | 0.0748 | 0.0139 | 0.0621 | 0.0623    | 19.0    | 0.0667    | -0.2237 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 3.0   | 126  | 3.6862          | 0.0738 | 0.0125 | 0.0628 | 0.0631    | 19.0    | 0.0577    | -0.2232 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 4.0   | 168  | 3.6754          | 0.0774 | 0.0129 | 0.0646 | 0.0649    | 19.0    | 0.0747    | -0.2113 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 5.0   | 210  | 3.6695          | 0.0775 | 0.0127 | 0.0644 | 0.0647    | 19.0    | 0.0733    | -0.2139 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log        | 6.0   | 252  | 3.6673          | 0.078  | 0.0127 | 0.0648 | 0.065     | 19.0    | 0.0673    | -0.2134 | -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_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\_old
============================


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


* Loss: 3.6673
* Rouge1: 0.078
* Rouge2: 0.0127
* Rougel: 0.0648
* Rougelsum: 0.065
* Gen Len: 19.0
* Precision: 0.0673
* Recall: -0.2134
* 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