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# le-fine-tune-mbart-large-50

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the ravkuk_summerize_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7762
- Rouge1: 0.2928
- Rouge2: 0.1926
- Rougel: 0.2815
- Rougelsum: 0.2816
- Gen Len: 34.5028

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.1169        | 1.0   | 197  | 2.3604          | 0.1878 | 0.0725 | 0.1737 | 0.1737    | 33.7784 |
| 1.8945        | 2.0   | 394  | 2.2522          | 0.1897 | 0.0765 | 0.1776 | 0.1776    | 34.2074 |
| 1.3083        | 3.0   | 591  | 2.2886          | 0.2001 | 0.0927 | 0.1895 | 0.1892    | 35.4432 |
| 0.8693        | 4.0   | 788  | 2.3727          | 0.2243 | 0.1123 | 0.2122 | 0.2117    | 31.4943 |
| 0.5507        | 5.0   | 985  | 2.5059          | 0.2577 | 0.1527 | 0.2463 | 0.2466    | 34.3693 |
| 0.3385        | 6.0   | 1182 | 2.6032          | 0.2703 | 0.1672 | 0.2593 | 0.2584    | 33.5994 |
| 0.2031        | 7.0   | 1379 | 2.6518          | 0.2912 | 0.1932 | 0.2812 | 0.281     | 34.1676 |
| 0.1272        | 8.0   | 1576 | 2.7040          | 0.2891 | 0.1895 | 0.2799 | 0.2796    | 34.6761 |
| 0.0842        | 9.0   | 1773 | 2.7515          | 0.2978 | 0.198  | 0.2888 | 0.2887    | 34.1932 |
| 0.0605        | 10.0  | 1970 | 2.7762          | 0.2928 | 0.1926 | 0.2815 | 0.2816    | 34.5028 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["summarization", "generated_from_trainer"], "datasets": ["ravkuk_summerize_dataset"], "metrics": ["rouge"], "model-index": [{"name": "le-fine-tune-mbart-large-50", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "ravkuk_summerize_dataset", "type": "ravkuk_summerize_dataset", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.2928, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard mbart text2text-generation summarization generated_from_trainer dataset:ravkuk_summerize_dataset license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

le-fine-tune-mbart-large-50
===========================


This model is a fine-tuned version of facebook/mbart-large-50 on the ravkuk\_summerize\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 2.7762
* Rouge1: 0.2928
* Rouge2: 0.1926
* Rougel: 0.2815
* Rougelsum: 0.2816
* Gen Len: 34.5028


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: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2