---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_book_corpus-ld
metrics:
- accuracy
model-index:
- name: bert_base_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: 0.7611995452621755
---

<!-- 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_base_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: 2.9459
- Accuracy: 0.7612

## 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: 96
- eval_batch_size: 96
- 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 |
|:-------------:|:-------:|:------:|:---------------:|:--------:|
| 7.6611        | 0.4215  | 10000  | 7.4831          | 0.1626   |
| 7.54          | 0.8431  | 20000  | 7.3893          | 0.1640   |
| 7.477         | 1.2646  | 30000  | 7.3399          | 0.1663   |
| 7.4478        | 1.6861  | 40000  | 7.3119          | 0.1667   |
| 5.084         | 2.1077  | 50000  | 4.6037          | 0.4982   |
| 4.1458        | 2.5292  | 60000  | 3.8418          | 0.6153   |
| 3.9219        | 2.9507  | 70000  | 3.6449          | 0.6468   |
| 3.8057        | 3.3723  | 80000  | 3.5337          | 0.6641   |
| 3.7243        | 3.7938  | 90000  | 3.4573          | 0.6761   |
| 3.6613        | 4.2153  | 100000 | 3.4046          | 0.6845   |
| 3.6198        | 4.6369  | 110000 | 3.3638          | 0.6910   |
| 3.5769        | 5.0584  | 120000 | 3.3235          | 0.6972   |
| 3.5446        | 5.4799  | 130000 | 3.2929          | 0.7021   |
| 3.5226        | 5.9014  | 140000 | 3.2670          | 0.7062   |
| 3.493         | 6.3230  | 150000 | 3.2465          | 0.7099   |
| 3.4784        | 6.7445  | 160000 | 3.2292          | 0.7130   |
| 3.4465        | 7.1660  | 170000 | 3.2074          | 0.7165   |
| 3.433         | 7.5876  | 180000 | 3.1960          | 0.7186   |
| 3.4171        | 8.0091  | 190000 | 3.1785          | 0.7212   |
| 3.4016        | 8.4306  | 200000 | 3.1640          | 0.7237   |
| 3.3853        | 8.8522  | 210000 | 3.1538          | 0.7257   |
| 3.3776        | 9.2737  | 220000 | 3.1404          | 0.7278   |
| 3.3568        | 9.6952  | 230000 | 3.1291          | 0.7295   |
| 3.3441        | 10.1168 | 240000 | 3.1205          | 0.7315   |
| 3.341         | 10.5383 | 250000 | 3.1095          | 0.7328   |
| 3.3292        | 10.9598 | 260000 | 3.1005          | 0.7345   |
| 3.3173        | 11.3814 | 270000 | 3.0944          | 0.7355   |
| 3.3162        | 11.8029 | 280000 | 3.0838          | 0.7373   |
| 3.298         | 12.2244 | 290000 | 3.0764          | 0.7386   |
| 3.293         | 12.6460 | 300000 | 3.0676          | 0.7400   |
| 3.2808        | 13.0675 | 310000 | 3.0613          | 0.7413   |
| 3.2786        | 13.4890 | 320000 | 3.0542          | 0.7423   |
| 3.2717        | 13.9106 | 330000 | 3.0505          | 0.7432   |
| 3.266         | 14.3321 | 340000 | 3.0427          | 0.7445   |
| 3.2583        | 14.7536 | 350000 | 3.0360          | 0.7454   |
| 3.2508        | 15.1751 | 360000 | 3.0327          | 0.7461   |
| 3.2454        | 15.5967 | 370000 | 3.0232          | 0.7474   |
| 3.2386        | 16.0182 | 380000 | 3.0186          | 0.7484   |
| 3.2334        | 16.4397 | 390000 | 3.0147          | 0.7492   |
| 3.2251        | 16.8613 | 400000 | 3.0106          | 0.7499   |
| 3.2228        | 17.2828 | 410000 | 3.0035          | 0.7511   |
| 3.2162        | 17.7043 | 420000 | 3.0003          | 0.7517   |
| 3.2079        | 18.1259 | 430000 | 2.9971          | 0.7524   |
| 3.2024        | 18.5474 | 440000 | 2.9928          | 0.7530   |
| 3.2014        | 18.9689 | 450000 | 2.9856          | 0.7541   |
| 3.1962        | 19.3905 | 460000 | 2.9826          | 0.7547   |
| 3.1917        | 19.8120 | 470000 | 2.9786          | 0.7555   |
| 3.1854        | 20.2335 | 480000 | 2.9755          | 0.7561   |
| 3.18          | 20.6551 | 490000 | 2.9711          | 0.7567   |
| 3.1744        | 21.0766 | 500000 | 2.9676          | 0.7573   |
| 3.1714        | 21.4981 | 510000 | 2.9635          | 0.7580   |
| 3.1664        | 21.9197 | 520000 | 2.9616          | 0.7583   |
| 3.1674        | 22.3412 | 530000 | 2.9592          | 0.7588   |
| 3.1614        | 22.7627 | 540000 | 2.9579          | 0.7591   |
| 3.1616        | 23.1843 | 550000 | 2.9536          | 0.7597   |
| 3.1579        | 23.6058 | 560000 | 2.9513          | 0.7603   |
| 3.154         | 24.0273 | 570000 | 2.9501          | 0.7605   |
| 3.1504        | 24.4488 | 580000 | 2.9464          | 0.7610   |
| 3.1482        | 24.8704 | 590000 | 2.9464          | 0.7610   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1