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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 |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert_lda_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 |
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type: gokulsrinivasagan/processed_book_corpus-ld |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.731461833901348 |
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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_v1_book |
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This model is a fine-tuned version of [](https://huggingface.co/) on the gokulsrinivasagan/processed_book_corpus-ld dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1051 |
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- Accuracy: 0.7315 |
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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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| 7.6644 | 0.4215 | 10000 | 7.4872 | 0.1609 | |
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| 4.6569 | 0.8431 | 20000 | 4.2985 | 0.5609 | |
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| 4.2453 | 1.2646 | 30000 | 3.9352 | 0.6094 | |
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| 4.0748 | 1.6861 | 40000 | 3.7740 | 0.6315 | |
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| 3.9464 | 2.1077 | 50000 | 3.6555 | 0.6453 | |
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| 3.8728 | 2.5292 | 60000 | 3.5809 | 0.6566 | |
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| 3.814 | 2.9507 | 70000 | 3.5364 | 0.6635 | |
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| 3.771 | 3.3723 | 80000 | 3.4922 | 0.6700 | |
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| 3.735 | 3.7938 | 90000 | 3.4582 | 0.6753 | |
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| 3.7016 | 4.2153 | 100000 | 3.4345 | 0.6790 | |
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| 3.681 | 4.6369 | 110000 | 3.4123 | 0.6824 | |
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| 3.6573 | 5.0584 | 120000 | 3.3854 | 0.6861 | |
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| 3.6373 | 5.4799 | 130000 | 3.3676 | 0.6889 | |
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| 3.6238 | 5.9014 | 140000 | 3.3501 | 0.6915 | |
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| 3.6004 | 6.3230 | 150000 | 3.3354 | 0.6939 | |
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| 3.5931 | 6.7445 | 160000 | 3.3241 | 0.6959 | |
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| 3.5703 | 7.1660 | 170000 | 3.3077 | 0.6986 | |
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| 3.5616 | 7.5876 | 180000 | 3.3021 | 0.6993 | |
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| 3.5502 | 8.0091 | 190000 | 3.2892 | 0.7014 | |
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| 3.5388 | 8.4306 | 200000 | 3.2785 | 0.7033 | |
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| 3.5264 | 8.8522 | 210000 | 3.2708 | 0.7046 | |
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| 3.5212 | 9.2737 | 220000 | 3.2598 | 0.7061 | |
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| 3.5045 | 9.6952 | 230000 | 3.2526 | 0.7073 | |
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| 3.4939 | 10.1168 | 240000 | 3.2483 | 0.7087 | |
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| 3.4934 | 10.5383 | 250000 | 3.2361 | 0.7101 | |
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| 3.4833 | 10.9598 | 260000 | 3.2301 | 0.7111 | |
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| 3.4747 | 11.3814 | 270000 | 3.2252 | 0.7120 | |
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| 3.4753 | 11.8029 | 280000 | 3.2172 | 0.7129 | |
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| 3.46 | 12.2244 | 290000 | 3.2102 | 0.7141 | |
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| 3.457 | 12.6460 | 300000 | 3.2041 | 0.7154 | |
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| 3.4464 | 13.0675 | 310000 | 3.1984 | 0.7163 | |
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| 3.4446 | 13.4890 | 320000 | 3.1933 | 0.7171 | |
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| 3.4398 | 13.9106 | 330000 | 3.1897 | 0.7174 | |
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| 3.436 | 14.3321 | 340000 | 3.1838 | 0.7185 | |
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| 3.4289 | 14.7536 | 350000 | 3.1784 | 0.7193 | |
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| 3.4223 | 15.1751 | 360000 | 3.1748 | 0.7198 | |
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| 3.4187 | 15.5967 | 370000 | 3.1676 | 0.7208 | |
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| 3.414 | 16.0182 | 380000 | 3.1651 | 0.7216 | |
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| 3.409 | 16.4397 | 390000 | 3.1609 | 0.7222 | |
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| 3.4022 | 16.8613 | 400000 | 3.1584 | 0.7226 | |
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| 3.4019 | 17.2828 | 410000 | 3.1511 | 0.7238 | |
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| 3.395 | 17.7043 | 420000 | 3.1483 | 0.7241 | |
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| 3.3878 | 18.1259 | 430000 | 3.1473 | 0.7248 | |
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| 3.3833 | 18.5474 | 440000 | 3.1439 | 0.7250 | |
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| 3.3828 | 18.9689 | 450000 | 3.1381 | 0.7260 | |
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| 3.3795 | 19.3905 | 460000 | 3.1349 | 0.7265 | |
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| 3.3746 | 19.8120 | 470000 | 3.1318 | 0.7272 | |
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| 3.3704 | 20.2335 | 480000 | 3.1287 | 0.7275 | |
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| 3.366 | 20.6551 | 490000 | 3.1248 | 0.7283 | |
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| 3.3621 | 21.0766 | 500000 | 3.1214 | 0.7286 | |
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| 3.3582 | 21.4981 | 510000 | 3.1189 | 0.7291 | |
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| 3.3547 | 21.9197 | 520000 | 3.1174 | 0.7294 | |
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| 3.3561 | 22.3412 | 530000 | 3.1152 | 0.7298 | |
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| 3.3516 | 22.7627 | 540000 | 3.1145 | 0.7300 | |
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| 3.3517 | 23.1843 | 550000 | 3.1110 | 0.7303 | |
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| 3.349 | 23.6058 | 560000 | 3.1087 | 0.7309 | |
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| 3.3446 | 24.0273 | 570000 | 3.1080 | 0.7311 | |
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| 3.342 | 24.4488 | 580000 | 3.1042 | 0.7317 | |
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| 3.3397 | 24.8704 | 590000 | 3.1048 | 0.7314 | |
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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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