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# bart-base-arxiv-sum-session-1

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the arxiv_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8862
- Rouge1: 12.7479
- Rouge2: 4.8295
- Rougel: 10.2761
- Rougelsum: 11.7334

## Model description

Model obtained from fine-tuning facebook/bart-base on 25,000 training samples from the ccdv/arxiv-summarization dataset.

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log        | 1.0   | 195  | 2.9794          | 12.5852 | 4.6927 | 10.1374 | 11.6014   |
| No log        | 2.0   | 390  | 2.9077          | 12.5854 | 4.7568 | 10.166  | 11.5699   |
| No log        | 3.0   | 585  | 2.8862          | 12.7479 | 4.8295 | 10.2761 | 11.7334   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["arxiv_summarization_dataset", "ccdv/arxiv-summarization"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-arxiv-sum-session-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "arxiv_summarization_dataset", "type": "arxiv_summarization_dataset", "config": "section", "split": "validation", "args": "section"}, "metrics": [{"type": "rouge", "value": 12.7479, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation summarization generated_from_trainer dataset:arxiv_summarization_dataset dataset:ccdv/arxiv-summarization license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-arxiv_summarization_dataset #dataset-ccdv/arxiv-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bart-base-arxiv-sum-session-1
=============================


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


* Loss: 2.8862
* Rouge1: 12.7479
* Rouge2: 4.8295
* Rougel: 10.2761
* Rougelsum: 11.7334


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


Model obtained from fine-tuning facebook/bart-base on 25,000 training samples from the ccdv/arxiv-summarization dataset.


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-arxiv_summarization_dataset #dataset-ccdv/arxiv-summarization #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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-arxiv_summarization_dataset #dataset-ccdv/arxiv-summarization #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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2