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--- |
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library_name: transformers |
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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-ld-100 |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert_lda_100_v1_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-ld-100 |
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type: gokulsrinivasagan/processed_book_corpus-ld-100 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7269170235533601 |
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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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# distilbert_lda_100_v1_book |
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This model is a fine-tuned version of [](https://huggingface.co/) on the gokulsrinivasagan/processed_book_corpus-ld-100 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.6471 |
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- Accuracy: 0.7269 |
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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 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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| 9.8243 | 0.4215 | 10000 | 9.5492 | 0.1639 | |
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| 6.4155 | 0.8431 | 20000 | 6.0168 | 0.5575 | |
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| 5.9278 | 1.2646 | 30000 | 5.5919 | 0.6085 | |
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| 5.7301 | 1.6861 | 40000 | 5.4051 | 0.6304 | |
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| 5.5965 | 2.1077 | 50000 | 5.2807 | 0.6431 | |
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| 5.5012 | 2.5292 | 60000 | 5.1835 | 0.6540 | |
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| 5.4266 | 2.9507 | 70000 | 5.1245 | 0.6611 | |
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| 5.3773 | 3.3723 | 80000 | 5.0742 | 0.6676 | |
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| 5.3364 | 3.7938 | 90000 | 5.0321 | 0.6726 | |
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| 5.2973 | 4.2153 | 100000 | 5.0044 | 0.6767 | |
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| 5.2724 | 4.6369 | 110000 | 4.9772 | 0.6799 | |
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| 5.2442 | 5.0584 | 120000 | 4.9517 | 0.6836 | |
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| 5.2231 | 5.4799 | 130000 | 4.9291 | 0.6863 | |
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| 5.2074 | 5.9014 | 140000 | 4.9105 | 0.6888 | |
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| 5.1812 | 6.3230 | 150000 | 4.8956 | 0.6911 | |
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| 5.1733 | 6.7445 | 160000 | 4.8813 | 0.6934 | |
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| 5.147 | 7.1660 | 170000 | 4.8666 | 0.6953 | |
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| 5.1368 | 7.5876 | 180000 | 4.8567 | 0.6967 | |
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| 5.1244 | 8.0091 | 190000 | 4.8440 | 0.6982 | |
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| 5.1142 | 8.4306 | 200000 | 4.8315 | 0.6998 | |
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| 5.1017 | 8.8522 | 210000 | 4.8245 | 0.7013 | |
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| 5.0955 | 9.2737 | 220000 | 4.8129 | 0.7025 | |
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| 5.0784 | 9.6952 | 230000 | 4.8042 | 0.7039 | |
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| 5.0662 | 10.1168 | 240000 | 4.7974 | 0.7053 | |
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| 5.067 | 10.5383 | 250000 | 4.7871 | 0.7062 | |
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| 5.0545 | 10.9598 | 260000 | 4.7792 | 0.7074 | |
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| 5.0461 | 11.3814 | 270000 | 4.7762 | 0.7082 | |
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| 5.0456 | 11.8029 | 280000 | 4.7663 | 0.7093 | |
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| 5.0294 | 12.2244 | 290000 | 4.7599 | 0.7103 | |
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| 5.0258 | 12.6460 | 300000 | 4.7528 | 0.7113 | |
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| 5.0149 | 13.0675 | 310000 | 4.7464 | 0.7123 | |
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| 5.0114 | 13.4890 | 320000 | 4.7420 | 0.7131 | |
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| 5.0086 | 13.9106 | 330000 | 4.7378 | 0.7137 | |
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| 5.004 | 14.3321 | 340000 | 4.7310 | 0.7147 | |
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| 4.9941 | 14.7536 | 350000 | 4.7263 | 0.7152 | |
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| 4.9902 | 15.1751 | 360000 | 4.7222 | 0.7157 | |
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| 4.9867 | 15.5967 | 370000 | 4.7158 | 0.7168 | |
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| 4.9796 | 16.0182 | 380000 | 4.7116 | 0.7175 | |
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| 4.9751 | 16.4397 | 390000 | 4.7051 | 0.7180 | |
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| 4.9683 | 16.8613 | 400000 | 4.7038 | 0.7184 | |
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| 4.967 | 17.2828 | 410000 | 4.6955 | 0.7196 | |
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| 4.961 | 17.7043 | 420000 | 4.6947 | 0.7200 | |
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| 4.953 | 18.1259 | 430000 | 4.6910 | 0.7204 | |
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| 4.9491 | 18.5474 | 440000 | 4.6884 | 0.7208 | |
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| 4.9485 | 18.9689 | 450000 | 4.6825 | 0.7217 | |
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| 4.9439 | 19.3905 | 460000 | 4.6790 | 0.7222 | |
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| 4.9417 | 19.8120 | 470000 | 4.6757 | 0.7226 | |
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| 4.9334 | 20.2335 | 480000 | 4.6713 | 0.7233 | |
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| 4.929 | 20.6551 | 490000 | 4.6686 | 0.7238 | |
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| 4.925 | 21.0766 | 500000 | 4.6645 | 0.7242 | |
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| 4.9207 | 21.4981 | 510000 | 4.6618 | 0.7246 | |
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| 4.9177 | 21.9197 | 520000 | 4.6599 | 0.7250 | |
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| 4.9191 | 22.3412 | 530000 | 4.6584 | 0.7252 | |
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| 4.9138 | 22.7627 | 540000 | 4.6577 | 0.7255 | |
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| 4.9139 | 23.1843 | 550000 | 4.6533 | 0.7259 | |
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| 4.9098 | 23.6058 | 560000 | 4.6508 | 0.7264 | |
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| 4.9063 | 24.0273 | 570000 | 4.6497 | 0.7265 | |
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| 4.9048 | 24.4488 | 580000 | 4.6457 | 0.7271 | |
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| 4.9011 | 24.8704 | 590000 | 4.6463 | 0.7270 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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