---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: video_transcript_summary
  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. -->

# video_transcript_summary

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5472
- Rouge1: 0.3909
- Rouge2: 0.1328
- Rougel: 0.2802
- Rougelsum: 0.2801
- Gen Len: 69.8235

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 5    | 2.6381          | 0.3163 | 0.0838 | 0.2135 | 0.2138    | 72.1176 |
| No log        | 2.0   | 10   | 2.4739          | 0.345  | 0.0969 | 0.2372 | 0.2364    | 69.7647 |
| No log        | 3.0   | 15   | 2.6011          | 0.3719 | 0.116  | 0.2619 | 0.2605    | 69.7059 |
| No log        | 4.0   | 20   | 2.7476          | 0.3769 | 0.1156 | 0.2588 | 0.2572    | 72.9412 |
| No log        | 5.0   | 25   | 2.9283          | 0.3812 | 0.1206 | 0.2645 | 0.2625    | 74.4706 |
| No log        | 6.0   | 30   | 3.3089          | 0.3722 | 0.1194 | 0.2779 | 0.2769    | 70.1765 |
| No log        | 7.0   | 35   | 3.6585          | 0.3802 | 0.1177 | 0.2718 | 0.2719    | 73.2941 |
| No log        | 8.0   | 40   | 3.8924          | 0.3764 | 0.1056 | 0.2661 | 0.2651    | 70.8235 |
| No log        | 9.0   | 45   | 4.3638          | 0.3677 | 0.1144 | 0.2653 | 0.2634    | 74.0    |
| No log        | 10.0  | 50   | 4.5590          | 0.354  | 0.1069 | 0.2491 | 0.2477    | 71.0    |
| No log        | 11.0  | 55   | 4.5746          | 0.4103 | 0.1359 | 0.2935 | 0.293     | 72.5882 |
| No log        | 12.0  | 60   | 4.9055          | 0.3869 | 0.118  | 0.2705 | 0.2695    | 66.1176 |
| No log        | 13.0  | 65   | 5.0987          | 0.3947 | 0.1292 | 0.2713 | 0.2716    | 74.7647 |
| No log        | 14.0  | 70   | 5.3199          | 0.3814 | 0.1135 | 0.2839 | 0.2816    | 72.2353 |
| No log        | 15.0  | 75   | 5.4462          | 0.4092 | 0.1265 | 0.2896 | 0.29      | 68.0    |
| No log        | 16.0  | 80   | 5.4262          | 0.412  | 0.1282 | 0.293  | 0.2932    | 72.0    |
| No log        | 17.0  | 85   | 5.4873          | 0.3776 | 0.1051 | 0.2637 | 0.2636    | 68.8824 |
| No log        | 18.0  | 90   | 5.6043          | 0.3932 | 0.1106 | 0.2734 | 0.2727    | 67.9412 |
| No log        | 19.0  | 95   | 5.7495          | 0.3694 | 0.1075 | 0.2699 | 0.2692    | 69.7647 |
| No log        | 20.0  | 100  | 5.6518          | 0.3849 | 0.1109 | 0.2714 | 0.2709    | 65.5882 |
| No log        | 21.0  | 105  | 5.4524          | 0.3762 | 0.1101 | 0.2598 | 0.2596    | 64.5294 |
| No log        | 22.0  | 110  | 5.5472          | 0.3909 | 0.1328 | 0.2802 | 0.2801    | 69.8235 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1