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# bart-base-multi-news

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4147
- Rouge1: 26.31
- Rouge2: 9.6
- Rougel: 20.87
- Rougelsum: 21.54

## Intended uses & limitations

The inteded use of this model is text summarization.
The model requires additional training in order to perform better in the task of summarization.

## Training and evaluation data

The training data were 10000 samples from the multi-news training dataset
and the evaluation data were 500 samples from the multi-news evaluation dataset

## Training procedure

For the training procedure the Seq2SeqTrainer class was used from the transformers library.

### Training hyperparameters

The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.4041        | 1.0   | 1250 | 2.4147          | 26.31  | 9.6    | 20.87  | 21.54     |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "rouge", "value": 26.31, "name": "Rouge1"}, {"type": "rouge", "value": 9.6, "name": "Rouge2"}, {"type": "rouge", "value": 20.87, "name": "Rougel"}, {"type": "rouge", "value": 21.54, "name": "Rougelsum"}]}]}]}
transformers pytorch tensorboard safetensors bart text2text-generation summarization generated_from_trainer en dataset:multi_news base_model:facebook/bart-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #summarization #generated_from_trainer #en #dataset-multi_news #base_model-facebook/bart-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bart-base-multi-news
====================


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


* Loss: 2.4147
* Rouge1: 26.31
* Rouge2: 9.6
* Rougel: 20.87
* Rougelsum: 21.54


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


The inteded use of this model is text summarization.
The model requires additional training in order to perform better in the task of summarization.


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


The training data were 10000 samples from the multi-news training dataset
and the evaluation data were 500 samples from the multi-news evaluation dataset


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


For the training procedure the Seq2SeqTrainer class was used from the transformers library.


### Training hyperparameters


The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #summarization #generated_from_trainer #en #dataset-multi_news #base_model-facebook/bart-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3