---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-science-v3-e1-v4-e6-manual
  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. -->

# bart-cnn-science-v3-e1-v4-e6-manual

This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4513
- Rouge1: 51.4471
- Rouge2: 31.5595
- Rougel: 31.7717
- Rougelsum: 49.4999
- Gen Len: 142.0

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 42   | 1.0691          | 51.1883 | 31.2479 | 33.7004 | 48.9571   | 142.0   |
| No log        | 2.0   | 84   | 1.0883          | 51.7634 | 29.8573 | 30.7155 | 49.3378   | 142.0   |
| No log        | 3.0   | 126  | 1.2355          | 52.9606 | 31.3539 | 33.5131 | 49.9275   | 142.0   |
| No log        | 4.0   | 168  | 1.3430          | 52.2108 | 32.7896 | 34.65   | 50.4271   | 139.1   |
| No log        | 5.0   | 210  | 1.3963          | 51.5335 | 30.4157 | 31.5759 | 49.6904   | 142.0   |
| No log        | 6.0   | 252  | 1.4513          | 51.4471 | 31.5595 | 31.7717 | 49.4999   | 142.0   |


### Framework versions

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