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# bart-large-cnn-finetuned-roundup-16

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8957
- Rouge1: 49.4097
- Rouge2: 29.3516
- Rougel: 31.527
- Rougelsum: 46.4241
- Gen Len: 141.9

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 132  | 1.3170          | 48.412  | 29.2017 | 31.6679 | 45.494    | 141.85  |
| No log        | 2.0   | 264  | 1.2292          | 49.0133 | 29.6645 | 30.7612 | 46.1673   | 142.0   |
| No log        | 3.0   | 396  | 1.2670          | 49.183  | 29.4104 | 31.573  | 46.7082   | 142.0   |
| 0.9596        | 4.0   | 528  | 1.3059          | 47.3854 | 26.6865 | 28.4666 | 44.4934   | 141.8   |
| 0.9596        | 5.0   | 660  | 1.3288          | 48.1189 | 26.9242 | 31.2938 | 45.3462   | 142.0   |
| 0.9596        | 6.0   | 792  | 1.4084          | 47.5713 | 26.7488 | 29.2959 | 45.1764   | 141.3   |
| 0.9596        | 7.0   | 924  | 1.5043          | 46.5407 | 26.0995 | 29.9007 | 43.9335   | 142.0   |
| 0.3369        | 8.0   | 1056 | 1.5115          | 49.6891 | 29.0514 | 32.33   | 46.9357   | 142.0   |
| 0.3369        | 9.0   | 1188 | 1.6131          | 47.5773 | 27.6348 | 30.5294 | 45.1151   | 142.0   |
| 0.3369        | 10.0  | 1320 | 1.6837          | 46.5699 | 26.3805 | 29.8581 | 43.5252   | 142.0   |
| 0.3369        | 11.0  | 1452 | 1.7874          | 47.1383 | 26.535  | 30.1724 | 44.2508   | 142.0   |
| 0.148         | 12.0  | 1584 | 1.7776          | 49.8061 | 30.1994 | 33.2405 | 47.6102   | 142.0   |
| 0.148         | 13.0  | 1716 | 1.8144          | 48.4451 | 28.2949 | 30.9026 | 45.6614   | 142.0   |
| 0.148         | 14.0  | 1848 | 1.8646          | 50.1964 | 30.4426 | 32.8156 | 47.4134   | 142.0   |
| 0.148         | 15.0  | 1980 | 1.8829          | 48.8129 | 29.2358 | 32.3247 | 46.2233   | 142.0   |
| 0.0726        | 16.0  | 2112 | 1.8957          | 49.4097 | 29.3516 | 31.527  | 46.4241   | 141.9   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-16", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bart-large-cnn-finetuned-roundup-16
===================================


This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8957
* Rouge1: 49.4097
* Rouge2: 29.3516
* Rougel: 31.527
* Rougelsum: 46.4241
* Gen Len: 141.9


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1