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# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16

This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8960
- Rouge1: 57.7198
- Rouge2: 44.5711
- Rougel: 47.6281
- Rougelsum: 56.2372
- Gen Len: 142.0

## 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   | 398  | 0.8634          | 53.7416 | 34.3731 | 37.1193 | 51.3075   | 142.0   |
| 0.8276        | 2.0   | 796  | 0.8001          | 53.9975 | 35.1019 | 38.2722 | 51.7878   | 142.0   |
| 0.5311        | 3.0   | 1194 | 0.7988          | 53.409  | 34.3201 | 37.5443 | 50.738    | 142.0   |
| 0.3538        | 4.0   | 1592 | 0.7698          | 53.679  | 34.7209 | 37.7895 | 51.2497   | 142.0   |
| 0.3538        | 5.0   | 1990 | 0.7863          | 54.2493 | 36.0643 | 39.1249 | 51.9758   | 142.0   |
| 0.2367        | 6.0   | 2388 | 0.7810          | 54.4042 | 37.4276 | 41.529  | 52.1544   | 142.0   |
| 0.164         | 7.0   | 2786 | 0.8055          | 56.0408 | 39.6744 | 42.8323 | 54.163    | 142.0   |
| 0.1146        | 8.0   | 3184 | 0.8098          | 55.2046 | 38.5399 | 41.9178 | 53.0001   | 142.0   |
| 0.089         | 9.0   | 3582 | 0.8199          | 57.1523 | 41.7614 | 44.5914 | 55.1602   | 142.0   |
| 0.089         | 10.0  | 3980 | 0.8644          | 56.943  | 41.5063 | 44.4929 | 54.9515   | 142.0   |
| 0.0647        | 11.0  | 4378 | 0.8413          | 57.0321 | 41.964  | 45.3971 | 55.0957   | 142.0   |
| 0.0485        | 12.0  | 4776 | 0.8735          | 56.7275 | 41.8577 | 44.3911 | 54.9824   | 142.0   |
| 0.0365        | 13.0  | 5174 | 0.8858          | 57.6103 | 43.8831 | 47.0374 | 56.0675   | 142.0   |
| 0.0271        | 14.0  | 5572 | 0.8974          | 57.39   | 42.8693 | 45.9344 | 55.7404   | 142.0   |
| 0.0271        | 15.0  | 5970 | 0.8990          | 57.9433 | 44.7301 | 47.843  | 56.5407   | 142.0   |
| 0.0232        | 16.0  | 6368 | 0.8960          | 57.7198 | 44.5711 | 47.6281 | 56.2372   | 142.0   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16", "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-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16
===============================================


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


* Loss: 0.8960
* Rouge1: 57.7198
* Rouge2: 44.5711
* Rougel: 47.6281
* Rougelsum: 56.2372
* Gen Len: 142.0


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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1