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# bart-large-cnn-finetuned-multi-news

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0950
- Rouge1: 42.0423
- Rouge2: 14.8812
- Rougel: 23.3412
- Rougelsum: 36.2613

## Model description

bart-large-cnn fine tuned on sample of multi-news dataset

## Intended uses & limitations

The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated.

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.2037        | 1.0   | 750  | 2.0950          | 42.0423 | 14.8812 | 23.3412 | 36.2613   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "args": "default"}, "metrics": [{"type": "rouge", "value": 42.0423, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer dataset:multi_news license:mit model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

bart-large-cnn-finetuned-multi-news
===================================


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


* Loss: 2.0950
* Rouge1: 42.0423
* Rouge2: 14.8812
* Rougel: 23.3412
* Rougelsum: 36.2613


Model description
-----------------


bart-large-cnn fine tuned on sample of multi-news dataset


Intended uses & limitations
---------------------------


The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated.


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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6