---
tags:
- summarization
- en
- seq2seq
- mbart
- Abstractive Summarization
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: mbert-finetune-en-cnn
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 40.5167
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 17.9329
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 27.6258
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 37.389
      verified: true
    - name: loss
      type: loss
      value: 2.2861809730529785
      verified: true
    - name: gen_len
      type: gen_len
      value: 87.9999
      verified: true
---

<!-- 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. -->

# mbert-finetune-en-cnn

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5577
- Rouge-1: 37.69
- Rouge-2: 16.47
- Rouge-l: 35.53
- Gen Len: 79.93
- Bertscore: 74.92

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1