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--- |
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base_model: kaizerBox/retnet-summarization |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xsum |
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model-index: |
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- name: retnet-summarization |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# retnet-summarization |
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This model is a fine-tuned version of [kaizerBox/retnet-summarization](https://huggingface.co/kaizerBox/retnet-summarization) on the xsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1397 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 3.4307 | 1.0 | 11525 | 3.3046 | |
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| 3.2601 | 2.0 | 23050 | 3.1760 | |
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| 3.1144 | 3.0 | 34575 | 3.1397 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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