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# multi-news-diff-weight

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: 2.3427
- Rouge1: 9.815
- Rouge2: 3.8774
- Rougel: 7.6169
- Rougelsum: 8.9863

## 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: 5e-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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.75          | 1.0   | 19225 | 2.4494          | 9.5021 | 3.5429 | 7.3531 | 8.6912    |
| 2.456         | 2.0   | 38450 | 2.3665          | 9.8103 | 3.8494 | 7.6256 | 8.9991    |
| 2.285         | 3.0   | 57675 | 2.3427          | 9.815  | 3.8774 | 7.6169 | 8.9863    |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "multi-news-diff-weight", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "config": "default", "split": "train[:95%]", "args": "default"}, "metrics": [{"type": "rouge", "value": 9.815, "name": "Rouge1"}]}]}]}
transformers pytorch bart text2text-generation summarization generated_from_trainer dataset:multi_news license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

multi-news-diff-weight
======================


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: 2.3427
* Rouge1: 9.815
* Rouge2: 3.8774
* Rougel: 7.6169
* Rougelsum: 8.9863


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: 5e-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: 3


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3