---
license: mit
base_model: alexdg19/bert_large_xsum_samsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bert_large_xsum_samsum2
  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: 0.6112
---

<!-- 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_samsum2

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: 1.1949
- Rouge1: 0.6112
- Rouge2: 0.3855
- Rougel: 0.5301
- Rougelsum: 0.5296
- Gen Len: 30.5427

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 41   | 0.9966          | 0.6323 | 0.416  | 0.5587 | 0.5598    | 26.9573 |
| No log        | 2.0   | 82   | 1.0976          | 0.6279 | 0.413  | 0.5569 | 0.5583    | 27.8171 |
| No log        | 3.0   | 123  | 1.1576          | 0.6236 | 0.4141 | 0.553  | 0.5537    | 29.5183 |
| No log        | 4.0   | 164  | 1.1998          | 0.6148 | 0.3948 | 0.5402 | 0.541     | 30.5061 |
| No log        | 5.0   | 205  | 1.1949          | 0.6112 | 0.3855 | 0.5301 | 0.5296    | 30.5427 |


### Framework versions

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