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library_name: transformers
license: apache-2.0
base_model: sshleifer/distilbart-xsum-6-6
tags:
- generated_from_trainer
model-index:
- name: distilbart-summarization-base
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. -->
# distilbart-summarization-base
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2566
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.699 | 0.1882 | 500 | 2.5648 |
| 2.5321 | 0.3764 | 1000 | 2.4413 |
| 2.4701 | 0.5645 | 1500 | 2.3791 |
| 2.4213 | 0.7527 | 2000 | 2.3353 |
| 2.404 | 0.9409 | 2500 | 2.3089 |
| 2.3352 | 1.1291 | 3000 | 2.2903 |
| 2.2998 | 1.3173 | 3500 | 2.2765 |
| 2.2999 | 1.5055 | 4000 | 2.2673 |
| 2.2665 | 1.6936 | 4500 | 2.2611 |
| 2.3412 | 1.8818 | 5000 | 2.2566 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|