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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: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gokulsrinivasagan/processed_book_corpus_cleaned |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert_base_train_book |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: gokulsrinivasagan/processed_book_corpus_cleaned |
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type: gokulsrinivasagan/processed_book_corpus_cleaned |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7530519758192171 |
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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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# bert_base_train_book |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the gokulsrinivasagan/processed_book_corpus_cleaned dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0775 |
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- Accuracy: 0.7531 |
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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: 96 |
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- eval_batch_size: 96 |
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- seed: 10 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10000 |
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- num_epochs: 25 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:------:|:---------------:|:--------:| |
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| 5.6277 | 0.4215 | 10000 | 5.4679 | 0.1648 | |
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| 5.5308 | 0.8431 | 20000 | 5.3921 | 0.1656 | |
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| 5.4819 | 1.2646 | 30000 | 5.3559 | 0.1668 | |
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| 5.4576 | 1.6861 | 40000 | 5.3327 | 0.1669 | |
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| 5.434 | 2.1077 | 50000 | 5.3193 | 0.1671 | |
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| 5.423 | 2.5292 | 60000 | 5.3064 | 0.1676 | |
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| 5.4078 | 2.9507 | 70000 | 5.3011 | 0.1670 | |
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| 5.3996 | 3.3723 | 80000 | 5.2891 | 0.1675 | |
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| 5.3864 | 3.7938 | 90000 | 5.2806 | 0.1672 | |
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| 5.3883 | 4.2153 | 100000 | 5.2894 | 0.1641 | |
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| 5.3743 | 4.6369 | 110000 | 5.2662 | 0.1678 | |
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| 5.3614 | 5.0584 | 120000 | 5.2495 | 0.1677 | |
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| 2.7786 | 5.4799 | 130000 | 2.4132 | 0.5314 | |
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| 2.191 | 5.9014 | 140000 | 1.8931 | 0.6135 | |
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| 1.997 | 6.3230 | 150000 | 1.7234 | 0.6414 | |
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| 1.8894 | 6.7445 | 160000 | 1.6208 | 0.6582 | |
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| 1.801 | 7.1660 | 170000 | 1.5466 | 0.6709 | |
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| 1.7429 | 7.5876 | 180000 | 1.4959 | 0.6795 | |
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| 1.6988 | 8.0091 | 190000 | 1.4521 | 0.6867 | |
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| 1.6587 | 8.4306 | 200000 | 1.4160 | 0.6930 | |
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| 1.6247 | 8.8522 | 210000 | 1.3884 | 0.6977 | |
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| 1.5996 | 9.2737 | 220000 | 1.3623 | 0.7023 | |
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| 1.5686 | 9.6952 | 230000 | 1.3387 | 0.7062 | |
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| 1.5445 | 10.1168 | 240000 | 1.3201 | 0.7099 | |
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| 1.5316 | 10.5383 | 250000 | 1.3002 | 0.7128 | |
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| 1.51 | 10.9598 | 260000 | 1.2850 | 0.7156 | |
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| 1.4938 | 11.3814 | 270000 | 1.2728 | 0.7178 | |
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| 1.4864 | 11.8029 | 280000 | 1.2574 | 0.7205 | |
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| 1.4641 | 12.2244 | 290000 | 1.2453 | 0.7228 | |
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| 1.4549 | 12.6460 | 300000 | 1.2324 | 0.7250 | |
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| 1.4394 | 13.0675 | 310000 | 1.2212 | 0.7270 | |
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| 1.4298 | 13.4890 | 320000 | 1.2135 | 0.7284 | |
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| 1.4227 | 13.9106 | 330000 | 1.2044 | 0.7299 | |
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| 1.414 | 14.3321 | 340000 | 1.1946 | 0.7319 | |
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| 1.4028 | 14.7536 | 350000 | 1.1855 | 0.7333 | |
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| 1.3929 | 15.1751 | 360000 | 1.1794 | 0.7344 | |
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| 1.3863 | 15.5967 | 370000 | 1.1696 | 0.7360 | |
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| 1.3762 | 16.0182 | 380000 | 1.1627 | 0.7372 | |
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| 1.3697 | 16.4397 | 390000 | 1.1562 | 0.7387 | |
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| 1.36 | 16.8613 | 400000 | 1.1513 | 0.7395 | |
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| 1.3566 | 17.2828 | 410000 | 1.1425 | 0.7411 | |
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| 1.3482 | 17.7043 | 420000 | 1.1388 | 0.7417 | |
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| 1.3398 | 18.1259 | 430000 | 1.1331 | 0.7430 | |
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| 1.3332 | 18.5474 | 440000 | 1.1295 | 0.7436 | |
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| 1.3316 | 18.9689 | 450000 | 1.1221 | 0.7448 | |
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| 1.3235 | 19.3905 | 460000 | 1.1177 | 0.7457 | |
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| 1.321 | 19.8120 | 470000 | 1.1127 | 0.7464 | |
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| 1.3123 | 20.2335 | 480000 | 1.1087 | 0.7474 | |
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| 1.3069 | 20.6551 | 490000 | 1.1046 | 0.7480 | |
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| 1.3016 | 21.0766 | 500000 | 1.0994 | 0.7486 | |
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| 1.2977 | 21.4981 | 510000 | 1.0952 | 0.7497 | |
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| 1.2929 | 21.9197 | 520000 | 1.0932 | 0.7500 | |
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| 1.2924 | 22.3412 | 530000 | 1.0899 | 0.7505 | |
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| 1.2862 | 22.7627 | 540000 | 1.0887 | 0.7510 | |
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| 1.2853 | 23.1843 | 550000 | 1.0847 | 0.7517 | |
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| 1.2827 | 23.6058 | 560000 | 1.0813 | 0.7523 | |
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| 1.2787 | 24.0273 | 570000 | 1.0805 | 0.7524 | |
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| 1.276 | 24.4488 | 580000 | 1.0765 | 0.7532 | |
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| 1.2732 | 24.8704 | 590000 | 1.0770 | 0.7530 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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