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# xsum_and_billsum_and_samsum

This model is a fine-tuned version of [psin/xsum_and_billsum](https://huggingface.co/psin/xsum_and_billsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8894
- Rouge1: 0.3798
- Rouge2: 0.15
- Rougel: 0.316
- Rougelsum: 0.3158
- Gen Len: 16.1416
- Precision: 0.4394
- Recall: 0.3237
- 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1  | Hashcode                                                           |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:---:|:------------------------------------------------------------------:|
| 2.1137        | 1.0   | 1842 | 1.8894          | 0.3798 | 0.15   | 0.316  | 0.3158    | 16.1416 | 0.4394    | 0.3237 | 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"], "datasets": ["samsum"], "metrics": ["rouge", "precision", "recall", "f1"], "model-index": [{"name": "xsum_and_billsum_and_samsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "test", "args": "samsum"}, "metrics": [{"type": "rouge", "value": 0.3798, "name": "Rouge1"}, {"type": "precision", "value": 0.4394, "name": "Precision"}, {"type": "recall", "value": 0.3237, "name": "Recall"}, {"type": "f1", "value": 0.0, "name": "F1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:samsum model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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


This model is a fine-tuned version of psin/xsum\_and\_billsum on the samsum dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8894
* Rouge1: 0.3798
* Rouge2: 0.15
* Rougel: 0.316
* Rougelsum: 0.3158
* Gen Len: 16.1416
* Precision: 0.4394
* Recall: 0.3237
* 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: 1
* 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: 1
* 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 #dataset-samsum #model-index #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: 1
* 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