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# distilbart-cnn-12-6-ftn-multi_news

This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8143
- Rouge1: 41.6136
- Rouge2: 14.7454
- Rougel: 23.3597
- Rougelsum: 36.1973
- Gen Len: 130.874

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.8821        | 0.89  | 2000 | 3.8143          | 41.6136 | 14.7454 | 23.3597 | 36.1973   | 130.874 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-12-6-ftn-multi_news", "results": [{"task": {"type": "summarization", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "args": "default"}, "metrics": [{"type": "rouge", "value": 41.6136, "name": "Rouge1"}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "multi_news", "type": "multi_news", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 39.6512, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 14.333, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 21.5797, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 35.5793, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 5.507579803466797, "name": "loss", "verified": true}, {"type": "gen_len", "value": 132.1745, "name": "gen_len", "verified": true}]}]}]}
transformers pytorch bart text2text-generation summarization dataset:multi_news license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilbart-cnn-12-6-ftn-multi\_news
===================================


This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the multi\_news dataset.
It achieves the following results on the evaluation set:


* Loss: 3.8143
* Rouge1: 41.6136
* Rouge2: 14.7454
* Rougel: 23.3597
* Rougelsum: 36.1973
* Gen Len: 130.874


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #dataset-multi_news #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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bart #text2text-generation #summarization #dataset-multi_news #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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1