transformers pytorch tensorboard mbart text2text-generation summarization generated_from_trainer dataset:ravkuk_summerize_dataset license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ravkuk_summerize_dataset
license: mit
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
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
---

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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
