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--- |
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license: apache-2.0 |
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base_model: google/flan-t5-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: flant5_sum_samsum |
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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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# flant5_sum_samsum |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co./google/flan-t5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Gen Len: 16.6760 |
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- Rouge Score: {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} |
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- Bleu Score: {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} |
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- Bleurt Score: -0.4863 |
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- Bert Score: [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Gen Len | Rouge Score | Bleu Score | Bleurt Score | Bert Score | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------:|:------------------------------------------------------------:| |
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| 0.0 | 1.0 | 921 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 2.0 | 1842 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 3.0 | 2763 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 4.0 | 3684 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 5.0 | 4605 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 6.0 | 5526 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 7.0 | 6447 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 8.0 | 7368 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 9.0 | 8289 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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| 0.0 | 10.0 | 9210 | nan | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863 | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.3 |
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