---
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-base-finetuned-test_6887
  results: []
---

<!-- 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. -->

# DanSumT5-base-finetuned-test_6887

This model is a fine-tuned version of [Danish-summarisation/DanSumT5-base](https://huggingface.co/Danish-summarisation/DanSumT5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5277
- Rouge1: 31.3188
- Rouge2: 7.8236
- Rougel: 17.8296
- Rougelsum: 28.6162
- Gen Len: 127.0

## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log        | 0.99  | 66   | 2.7401          | 29.9221 | 6.3861 | 16.5877 | 27.0611   | 125.09  |
| No log        | 1.99  | 133  | 2.6815          | 30.6874 | 6.9413 | 16.9609 | 27.8851   | 125.98  |
| No log        | 3.0   | 200  | 2.6440          | 31.2045 | 7.4012 | 17.7421 | 28.3497   | 126.63  |
| No log        | 4.0   | 267  | 2.6199          | 31.3329 | 7.4574 | 17.8549 | 28.643    | 126.98  |
| No log        | 4.99  | 333  | 2.5984          | 31.5184 | 7.7763 | 17.9153 | 29.0627   | 127.0   |
| No log        | 5.99  | 400  | 2.5822          | 31.8839 | 7.9755 | 18.0572 | 29.2282   | 126.65  |
| No log        | 7.0   | 467  | 2.5677          | 31.5939 | 7.9515 | 17.865  | 29.2019   | 126.45  |
| 2.8684        | 8.0   | 534  | 2.5587          | 31.4931 | 7.6042 | 17.6853 | 28.8366   | 126.79  |
| 2.8684        | 8.99  | 600  | 2.5496          | 31.105  | 7.6714 | 17.5128 | 28.5242   | 126.78  |
| 2.8684        | 9.99  | 667  | 2.5423          | 31.6087 | 8.0358 | 17.9956 | 28.9514   | 126.78  |
| 2.8684        | 11.0  | 734  | 2.5364          | 31.411  | 7.9534 | 17.895  | 28.7595   | 127.0   |
| 2.8684        | 12.0  | 801  | 2.5326          | 31.4648 | 7.9777 | 17.9589 | 28.8168   | 127.0   |
| 2.8684        | 12.99 | 867  | 2.5296          | 31.374  | 7.8341 | 17.8341 | 28.8146   | 127.0   |
| 2.8684        | 13.99 | 934  | 2.5278          | 31.2822 | 7.7789 | 17.7983 | 28.5903   | 127.0   |
| 2.8684        | 14.83 | 990  | 2.5277          | 31.3188 | 7.8236 | 17.8296 | 28.6162   | 127.0   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3