---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
datasets:
- mediasum
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Bart_mediasum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: mediasum
      type: mediasum
      config: roberta_prepended
      split: validation
      args: roberta_prepended
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3236
    - name: Precision
      type: precision
      value: 0.8858
    - name: Recall
      type: recall
      value: 0.8739
    - name: F1
      type: f1
      value: 0.8795
---

<!-- 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. -->

# Bart_mediasum

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the mediasum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9021
- Rouge1: 0.3236
- Rouge2: 0.1651
- Rougel: 0.2953
- Rougelsum: 0.2953
- Gen Len: 15.7946
- Precision: 0.8858
- Recall: 0.8739
- F1: 0.8795

## 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: 2e-05
- train_batch_size: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 2.1171        | 1.0   | 4621  | 2.0135          | 0.3138 | 0.1556 | 0.2853 | 0.2853    | 16.4704 | 0.8836    | 0.8717 | 0.8773 |
| 1.9804        | 2.0   | 9242  | 1.9440          | 0.3147 | 0.1581 | 0.2864 | 0.2866    | 16.2207 | 0.8831    | 0.8725 | 0.8775 |
| 1.8971        | 3.0   | 13863 | 1.9157          | 0.3209 | 0.1638 | 0.2925 | 0.2926    | 15.4676 | 0.8857    | 0.8733 | 0.8792 |
| 1.8449        | 4.0   | 18484 | 1.9021          | 0.3236 | 0.1651 | 0.2953 | 0.2953    | 15.7946 | 0.8858    | 0.8739 | 0.8795 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0