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--- |
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base_model: facebook/mbart-large-50 |
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library_name: peft |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mbart-large-50_Nepali_News_Summarization_0 |
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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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# mbart-large-50_Nepali_News_Summarization_0 |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3697 |
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- Rouge-1 R: 0.3809 |
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- Rouge-1 P: 0.3915 |
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- Rouge-1 F: 0.3761 |
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- Rouge-2 R: 0.215 |
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- Rouge-2 P: 0.2209 |
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- Rouge-2 F: 0.2109 |
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- Rouge-l R: 0.3708 |
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- Rouge-l P: 0.3809 |
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- Rouge-l F: 0.3661 |
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- Gen Len: 13.9732 |
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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: 0.0005 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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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 | Rouge-1 R | Rouge-1 P | Rouge-1 F | Rouge-2 R | Rouge-2 P | Rouge-2 F | Rouge-l R | Rouge-l P | Rouge-l F | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------:| |
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| 1.563 | 1.0 | 10191 | 1.4569 | 0.3599 | 0.3789 | 0.3581 | 0.1965 | 0.2082 | 0.1946 | 0.3499 | 0.3683 | 0.3481 | 13.8415 | |
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| 1.4053 | 2.0 | 20382 | 1.3963 | 0.3631 | 0.3927 | 0.367 | 0.203 | 0.2203 | 0.204 | 0.354 | 0.3827 | 0.3577 | 13.4601 | |
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| 1.2399 | 3.0 | 30573 | 1.3697 | 0.3809 | 0.3915 | 0.3761 | 0.215 | 0.2209 | 0.2109 | 0.3708 | 0.3809 | 0.3661 | 13.9732 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
