| | --- |
| | base_model: amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2 |
| | 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 [amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2](https://huggingface.co/amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9435 |
| |
|
| | ## 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 | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | 1.4132 | 0.99 | 16 | 1.1373 | |
| | | 1.3997 | 1.98 | 32 | 1.1288 | |
| | | 1.3814 | 2.97 | 48 | 1.1147 | |
| | | 1.5235 | 3.95 | 64 | 1.0936 | |
| | | 1.1194 | 4.94 | 80 | 1.0703 | |
| | | 1.356 | 5.99 | 97 | 1.0459 | |
| | | 1.2157 | 6.98 | 113 | 1.0206 | |
| | | 0.9685 | 7.97 | 129 | 0.9902 | |
| | | 1.2886 | 8.96 | 145 | 0.9639 | |
| | | 1.2373 | 9.88 | 160 | 0.9435 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.33.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.14.4 |
| | - Tokenizers 0.13.3 |
| |
|
