---
license: mit
base_model: facebook/mbart-large-50
tags:
- summarization
- generated_from_trainer
datasets:
- lr-sum
metrics:
- rouge
model-index:
- name: mbart-large-50-finetuned-lrsum-fr
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: lr-sum
      type: lr-sum
      config: fra
      split: validation
      args: fra
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2579
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mbart-large-50-finetuned-lrsum-fr

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the lr-sum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0341
- Rouge1: 0.2579
- Rouge2: 0.1232
- Rougel: 0.2142
- Rougelsum: 0.2153

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.8023        | 1.0   | 141  | 1.2331          | 0.2511 | 0.115  | 0.205  | 0.2088    |
| 8.3626        | 2.0   | 282  | 1.3380          | 0.2601 | 0.1213 | 0.2106 | 0.2155    |
| 0.848         | 3.0   | 423  | 1.5333          | 0.2431 | 0.1109 | 0.2008 | 0.2022    |
| 0.4302        | 4.0   | 564  | 1.4443          | 0.2487 | 0.1153 | 0.204  | 0.2063    |
| 0.2181        | 5.0   | 705  | 1.6967          | 0.2445 | 0.1081 | 0.1977 | 0.2001    |
| 0.1131        | 6.0   | 846  | 1.8275          | 0.2704 | 0.1358 | 0.2249 | 0.2265    |
| 0.052         | 7.0   | 987  | 1.9579          | 0.2549 | 0.1161 | 0.2085 | 0.2099    |
| 0.0245        | 8.0   | 1128 | 2.0341          | 0.2579 | 0.1232 | 0.2142 | 0.2153    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1