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# bart-large-finetuned-xsum

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the wsj_markets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8497
- Rouge1: 15.3934
- Rouge2: 7.0378
- Rougel: 13.9522
- Rougelsum: 14.3541
- Gen Len: 20.0

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0964        | 1.0   | 1735 | 0.9365          | 18.703  | 12.7539 | 18.1293 | 18.5397   | 20.0    |
| 0.95          | 2.0   | 3470 | 0.8871          | 19.5223 | 13.0938 | 18.9148 | 18.8363   | 20.0    |
| 0.8687        | 3.0   | 5205 | 0.8587          | 15.0915 | 7.142   | 13.6693 | 14.5975   | 20.0    |
| 0.7989        | 4.0   | 6940 | 0.8569          | 18.243  | 11.4495 | 17.4326 | 17.489    | 20.0    |
| 0.7493        | 5.0   | 8675 | 0.8497          | 15.3934 | 7.0378  | 13.9522 | 14.3541   | 20.0    |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.10.0
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wsj_markets"], "metrics": ["rouge"], "model_index": [{"name": "bart-large-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wsj_markets", "type": "wsj_markets", "args": "default"}, "metric": {"name": "Rouge1", "type": "rouge", "value": 15.3934}}]}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer dataset:wsj_markets license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wsj_markets #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bart-large-finetuned-xsum
=========================


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


* Loss: 0.8497
* Rouge1: 15.3934
* Rouge2: 7.0378
* Rougel: 13.9522
* Rougelsum: 14.3541
* Gen Len: 20.0


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: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.10.0
* Tokenizers 0.10.3



### 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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.10.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wsj_markets #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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.10.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wsj_markets #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: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.10.0
* Tokenizers 0.10.3