transformers pytorch bart text2text-generation generated_from_trainer base_model:philschmid/bart-large-cnn-samsum license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: philschmid/bart-large-cnn-samsum
tags:
- generated_from_trainer
model-index:
- name: bart-model
  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-model

This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6169

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.487         | 0.8   | 10   | 1.2019          |
| 1.3092        | 1.61  | 20   | 0.9905          |
| 1.0316        | 2.41  | 30   | 0.7841          |
| 0.8111        | 3.22  | 40   | 0.6587          |
| 0.7191        | 4.02  | 50   | 0.5964          |
| 0.5906        | 4.82  | 60   | 0.5613          |
| 0.5351        | 5.63  | 70   | 0.5393          |
| 0.4696        | 6.43  | 80   | 0.5429          |
| 0.4249        | 7.24  | 90   | 0.5287          |
| 0.3619        | 8.04  | 100  | 0.5577          |
| 0.3303        | 8.84  | 110  | 0.5794          |
| 0.2718        | 9.65  | 120  | 0.6169          |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
