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library_name: transformers
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_book_corpus-ld
metrics:
- accuracy
model-index:
- name: bert_tiny_lda_book
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokulsrinivasagan/processed_book_corpus-ld
type: gokulsrinivasagan/processed_book_corpus-ld
metrics:
- name: Accuracy
type: accuracy
value: 0.6827213137673483
---
<!-- 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_book
This model is a fine-tuned version of [](https://huggingface.co/) on the gokulsrinivasagan/processed_book_corpus-ld dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3915
- Accuracy: 0.6827
## 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 |
|:-------------:|:-------:|:------:|:---------------:|:--------:|
| 7.9444 | 0.7025 | 10000 | 7.7823 | 0.1644 |
| 5.4537 | 1.4051 | 20000 | 5.0087 | 0.4658 |
| 4.6397 | 2.1076 | 30000 | 4.2650 | 0.5607 |
| 4.3898 | 2.8102 | 40000 | 4.0379 | 0.5916 |
| 4.2383 | 3.5127 | 50000 | 3.8978 | 0.6113 |
| 4.1379 | 4.2153 | 60000 | 3.8117 | 0.6234 |
| 4.0736 | 4.9178 | 70000 | 3.7462 | 0.6324 |
| 4.0187 | 5.6203 | 80000 | 3.6985 | 0.6391 |
| 3.9803 | 6.3229 | 90000 | 3.6644 | 0.6444 |
| 3.9462 | 7.0254 | 100000 | 3.6333 | 0.6485 |
| 3.9217 | 7.7280 | 110000 | 3.6064 | 0.6526 |
| 3.8974 | 8.4305 | 120000 | 3.5810 | 0.6558 |
| 3.8714 | 9.1331 | 130000 | 3.5696 | 0.6581 |
| 3.8565 | 9.8356 | 140000 | 3.5454 | 0.6613 |
| 3.8382 | 10.5381 | 150000 | 3.5310 | 0.6632 |
| 3.8272 | 11.2407 | 160000 | 3.5181 | 0.6647 |
| 3.8059 | 11.9432 | 170000 | 3.5012 | 0.6666 |
| 3.7935 | 12.6458 | 180000 | 3.4849 | 0.6683 |
| 3.7815 | 13.3483 | 190000 | 3.4784 | 0.6695 |
| 3.7719 | 14.0509 | 200000 | 3.4671 | 0.6710 |
| 3.7614 | 14.7534 | 210000 | 3.4574 | 0.6724 |
| 3.7509 | 15.4560 | 220000 | 3.4488 | 0.6740 |
| 3.7456 | 16.1585 | 230000 | 3.4445 | 0.6745 |
| 3.736 | 16.8610 | 240000 | 3.4378 | 0.6753 |
| 3.728 | 17.5636 | 250000 | 3.4330 | 0.6763 |
| 3.7223 | 18.2661 | 260000 | 3.4270 | 0.6772 |
| 3.7195 | 18.9687 | 270000 | 3.4210 | 0.6780 |
| 3.7104 | 19.6712 | 280000 | 3.4156 | 0.6790 |
| 3.7086 | 20.3738 | 290000 | 3.4105 | 0.6797 |
| 3.7002 | 21.0763 | 300000 | 3.4070 | 0.6803 |
| 3.698 | 21.7788 | 310000 | 3.4013 | 0.6812 |
| 3.6915 | 22.4814 | 320000 | 3.3987 | 0.6814 |
| 3.6909 | 23.1839 | 330000 | 3.3962 | 0.6818 |
| 3.6883 | 23.8865 | 340000 | 3.3933 | 0.6825 |
| 3.6867 | 24.5890 | 350000 | 3.3903 | 0.6829 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
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