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---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_book_corpus-ld-100
metrics:
- accuracy
model-index:
- name: bert_tiny_lda_100_v1_book
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokulsrinivasagan/processed_book_corpus-ld-100
type: gokulsrinivasagan/processed_book_corpus-ld-100
metrics:
- name: Accuracy
type: accuracy
value: 0.6765868602733887
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_tiny_lda_100_v1_book
This model is a fine-tuned version of [](https://huggingface.co/) on the gokulsrinivasagan/processed_book_corpus-ld-100 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9524
- Accuracy: 0.6766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 160
- eval_batch_size: 160
- seed: 10
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:------:|:---------------:|:--------:|
| 10.2267 | 0.7025 | 10000 | 10.0665 | 0.1636 |
| 7.2173 | 1.4051 | 20000 | 6.7451 | 0.4823 |
| 6.4475 | 2.1076 | 30000 | 6.0308 | 0.5509 |
| 6.1492 | 2.8102 | 40000 | 5.7522 | 0.5804 |
| 5.9566 | 3.5127 | 50000 | 5.5702 | 0.6009 |
| 5.8259 | 4.2153 | 60000 | 5.4546 | 0.6137 |
| 5.7396 | 4.9178 | 70000 | 5.3672 | 0.6239 |
| 5.6664 | 5.6203 | 80000 | 5.3074 | 0.6312 |
| 5.6155 | 6.3229 | 90000 | 5.2622 | 0.6366 |
| 5.5704 | 7.0254 | 100000 | 5.2177 | 0.6416 |
| 5.5381 | 7.7280 | 110000 | 5.1869 | 0.6460 |
| 5.5072 | 8.4305 | 120000 | 5.1572 | 0.6495 |
| 5.476 | 9.1331 | 130000 | 5.1399 | 0.6520 |
| 5.4586 | 9.8356 | 140000 | 5.1144 | 0.6554 |
| 5.4395 | 10.5381 | 150000 | 5.0980 | 0.6573 |
| 5.4279 | 11.2407 | 160000 | 5.0854 | 0.6588 |
| 5.4084 | 11.9432 | 170000 | 5.0694 | 0.6610 |
| 5.3943 | 12.6458 | 180000 | 5.0544 | 0.6627 |
| 5.3829 | 13.3483 | 190000 | 5.0477 | 0.6636 |
| 5.374 | 14.0509 | 200000 | 5.0361 | 0.6652 |
| 5.3602 | 14.7534 | 210000 | 5.0257 | 0.6666 |
| 5.3506 | 15.4560 | 220000 | 5.0155 | 0.6681 |
| 5.3443 | 16.1585 | 230000 | 5.0103 | 0.6687 |
| 5.3334 | 16.8610 | 240000 | 5.0030 | 0.6697 |
| 5.3252 | 17.5636 | 250000 | 4.9964 | 0.6705 |
| 5.3187 | 18.2661 | 260000 | 4.9904 | 0.6711 |
| 5.3167 | 18.9687 | 270000 | 4.9849 | 0.6723 |
| 5.3068 | 19.6712 | 280000 | 4.9791 | 0.6731 |
| 5.3031 | 20.3738 | 290000 | 4.9740 | 0.6736 |
| 5.2947 | 21.0763 | 300000 | 4.9701 | 0.6742 |
| 5.2931 | 21.7788 | 310000 | 4.9633 | 0.6752 |
| 5.2875 | 22.4814 | 320000 | 4.9602 | 0.6756 |
| 5.2841 | 23.1839 | 330000 | 4.9582 | 0.6758 |
| 5.2815 | 23.8865 | 340000 | 4.9541 | 0.6762 |
| 5.2811 | 24.5890 | 350000 | 4.9512 | 0.6766 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
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