
# distilbart-cnn-6-6-reddit

This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the reddit dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9883
- Rouge1: 0.1849
- Rouge2: 0.0437
- Rougel: 0.1273
- Rougelsum: 0.1601

## More information and training script

You can find more information about how this model was trained, including the actual training script in [this github repository](https://github.com/VerleysenNiels/arxiv-summarizer).

## Training and evaluation data

I made a split in a train and test set. The test size is 1% of the total dataset, which comes down to about 38k samples.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.13          | 1.0   | 238116 | 3.2736          | 0.1773 | 0.0392 | 0.1223 | 0.1539    |
| 2.8586        | 2.0   | 476232 | 3.0449          | 0.1846 | 0.0431 | 0.127  | 0.1601    |
| 2.7844        | 3.0   | 714348 | 2.9883          | 0.1849 | 0.0437 | 0.1273 | 0.1601    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["reddit"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-6-6-reddit", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "reddit", "type": "reddit", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.1849, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer dataset:reddit license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-reddit #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilbart-cnn-6-6-reddit
=========================


This model is a fine-tuned version of sshleifer/distilbart-cnn-6-6 on the reddit dataset.
It achieves the following results on the evaluation set:


* Loss: 2.9883
* Rouge1: 0.1849
* Rouge2: 0.0437
* Rougel: 0.1273
* Rougelsum: 0.1601


More information and training script
------------------------------------


You can find more information about how this model was trained, including the actual training script in this github repository.


Training and evaluation data
----------------------------


I made a split in a train and test set. The test size is 1% of the total dataset, which comes down to about 38k samples.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-reddit #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-reddit #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2