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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/flan-t5-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: flanT5_large_Task2_semantic_pred |
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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_large_Task2_semantic_pred |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9725 |
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- Accuracy: 0.7979 |
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- Precision: 0.8195 |
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- Recall: 0.7566 |
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- F1 score: 0.7868 |
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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: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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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 | Accuracy | Precision | Recall | F1 score | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 1.3836 | 0.4074 | 2500 | 1.7839 | 0.5072 | 0.0 | 0.0 | 0.0 | |
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| 1.3837 | 0.8147 | 5000 | 0.7528 | 0.5072 | 0.0 | 0.0 | 0.0 | |
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| 1.2525 | 1.2221 | 7500 | 0.7166 | 0.4928 | 0.4928 | 1.0 | 0.6603 | |
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| 1.2899 | 1.6295 | 10000 | 1.5233 | 0.6310 | 0.5765 | 0.9471 | 0.7167 | |
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| 1.4214 | 2.0368 | 12500 | 1.2841 | 0.6571 | 0.6021 | 0.8968 | 0.7205 | |
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| 1.4003 | 2.4442 | 15000 | 1.1805 | 0.6115 | 0.5615 | 0.9656 | 0.7101 | |
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| 1.3396 | 2.8516 | 17500 | 0.9273 | 0.6206 | 0.5683 | 0.9577 | 0.7133 | |
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| 1.3938 | 3.2589 | 20000 | 1.5375 | 0.6858 | 0.7322 | 0.5714 | 0.6419 | |
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| 1.4272 | 3.6663 | 22500 | 1.2804 | 0.7158 | 0.7548 | 0.6270 | 0.6850 | |
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| 1.2643 | 4.0737 | 25000 | 1.2878 | 0.7262 | 0.7692 | 0.6349 | 0.6957 | |
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| 1.3015 | 4.4810 | 27500 | 1.1752 | 0.7158 | 0.7532 | 0.6296 | 0.6859 | |
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| 1.3525 | 4.8884 | 30000 | 1.1726 | 0.7249 | 0.6946 | 0.7884 | 0.7385 | |
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| 1.2839 | 5.2957 | 32500 | 1.0721 | 0.7223 | 0.7586 | 0.6402 | 0.6944 | |
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| 1.2047 | 5.7031 | 35000 | 1.3045 | 0.7223 | 0.7089 | 0.7407 | 0.7245 | |
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| 1.226 | 6.1105 | 37500 | 1.1262 | 0.7366 | 0.7486 | 0.7011 | 0.7240 | |
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| 1.1001 | 6.5178 | 40000 | 1.2328 | 0.7445 | 0.7407 | 0.7407 | 0.7407 | |
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| 1.1083 | 6.9252 | 42500 | 1.1119 | 0.7353 | 0.7030 | 0.8016 | 0.7491 | |
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| 1.0281 | 7.3326 | 45000 | 1.1205 | 0.7471 | 0.7434 | 0.7434 | 0.7434 | |
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| 1.0396 | 7.7399 | 47500 | 1.1243 | 0.7640 | 0.7958 | 0.7011 | 0.7454 | |
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| 0.9913 | 8.1473 | 50000 | 0.9882 | 0.7823 | 0.8187 | 0.7169 | 0.7645 | |
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| 0.9364 | 8.5547 | 52500 | 1.1489 | 0.7705 | 0.7488 | 0.8042 | 0.7755 | |
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| 1.0081 | 8.9620 | 55000 | 0.9507 | 0.7810 | 0.7807 | 0.7725 | 0.7766 | |
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| 0.9026 | 9.3694 | 57500 | 1.0181 | 0.7888 | 0.7888 | 0.7804 | 0.7846 | |
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| 0.8961 | 9.7768 | 60000 | 0.9725 | 0.7979 | 0.8195 | 0.7566 | 0.7868 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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