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# distilbart-cnn-arxiv-pubmed-pubmed-v3-e16

This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8306
- Rouge1: 56.4519
- Rouge2: 41.6818
- Rougel: 44.7833
- Rougelsum: 54.6359
- Gen Len: 141.9815

## 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  | 1.1157          | 50.9487 | 31.3005 | 34.0145 | 48.6057   | 141.8519 |
| 1.3569        | 2.0   | 796  | 0.9688          | 53.0653 | 34.1855 | 37.0759 | 50.5942   | 141.2963 |
| 0.8704        | 3.0   | 1194 | 0.9053          | 53.9684 | 36.0388 | 38.6674 | 51.9604   | 142.0    |
| 0.6287        | 4.0   | 1592 | 0.8515          | 54.2379 | 36.4915 | 39.1393 | 51.6991   | 141.4074 |
| 0.6287        | 5.0   | 1990 | 0.8274          | 53.6806 | 34.8373 | 37.7369 | 51.239    | 141.6481 |
| 0.465         | 6.0   | 2388 | 0.8486          | 55.2534 | 39.1757 | 41.6366 | 53.2989   | 141.9259 |
| 0.3432        | 7.0   | 2786 | 0.8116          | 54.539  | 37.6314 | 40.5531 | 52.1997   | 141.3889 |
| 0.2577        | 8.0   | 3184 | 0.7976          | 54.8212 | 36.8347 | 40.6768 | 52.7785   | 142.0    |
| 0.204         | 9.0   | 3582 | 0.8010          | 53.9302 | 37.3523 | 40.135  | 52.139    | 141.7778 |
| 0.204         | 10.0  | 3980 | 0.8168          | 54.3151 | 38.0665 | 42.4112 | 52.4682   | 142.0    |
| 0.1663        | 11.0  | 4378 | 0.8171          | 54.7027 | 38.3117 | 42.0196 | 52.8821   | 142.0    |
| 0.135         | 12.0  | 4776 | 0.8202          | 54.1035 | 37.9154 | 40.7676 | 52.2509   | 142.0    |
| 0.1102        | 13.0  | 5174 | 0.8204          | 56.223  | 41.0947 | 44.0131 | 54.3353   | 142.0    |
| 0.0928        | 14.0  | 5572 | 0.8280          | 56.1637 | 41.0408 | 44.2931 | 54.5488   | 142.0    |
| 0.0928        | 15.0  | 5970 | 0.8273          | 56.2608 | 41.3855 | 44.4432 | 54.5778   | 142.0    |
| 0.0847        | 16.0  | 6368 | 0.8306          | 56.4519 | 41.6818 | 44.7833 | 54.6359   | 141.9815 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-arxiv-pubmed-pubmed-v3-e16", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
=========================================


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


* Loss: 0.8306
* Rouge1: 56.4519
* Rouge2: 41.6818
* Rougel: 44.7833
* Rougelsum: 54.6359
* Gen Len: 141.9815


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 2.2.0
* 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 2.2.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #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 2.2.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #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 2.2.0
* Tokenizers 0.12.1