---
license: mit
base_model: alexdg19/bert_large_xsum_samsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bert_large_xsum_samsum3
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.5313
---

<!-- 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. -->

# bert_large_xsum_samsum3

This model is a fine-tuned version of [alexdg19/bert_large_xsum_samsum](https://huggingface.co./alexdg19/bert_large_xsum_samsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2354
- Rouge1: 0.5313
- Rouge2: 0.2827
- Rougel: 0.4367
- Rougelsum: 0.4357
- Gen Len: 30.939

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 164  | 1.1370          | 0.5599 | 0.3246 | 0.4748 | 0.4743    | 29.0122 |
| No log        | 2.0   | 328  | 1.2659          | 0.5494 | 0.3033 | 0.4623 | 0.4612    | 27.0671 |
| No log        | 3.0   | 492  | 1.4188          | 0.5198 | 0.2726 | 0.436  | 0.4346    | 28.6768 |
| 0.6603        | 4.0   | 656  | 1.5628          | 0.5391 | 0.2905 | 0.4555 | 0.4553    | 28.6159 |
| 0.6603        | 5.0   | 820  | 1.9045          | 0.5237 | 0.2774 | 0.4326 | 0.4321    | 31.5854 |
| 0.6603        | 6.0   | 984  | 2.0670          | 0.5199 | 0.2689 | 0.4251 | 0.4243    | 31.8049 |
| 0.1722        | 7.0   | 1148 | 1.9653          | 0.5269 | 0.2703 | 0.4342 | 0.4333    | 28.5122 |
| 0.1722        | 8.0   | 1312 | 2.1921          | 0.5296 | 0.2765 | 0.4393 | 0.4387    | 31.8354 |
| 0.1722        | 9.0   | 1476 | 2.4336          | 0.5299 | 0.2825 | 0.4399 | 0.4388    | 31.7988 |
| 0.052         | 10.0  | 1640 | 2.2354          | 0.5313 | 0.2827 | 0.4367 | 0.4357    | 30.939  |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1