---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_book_corpus-ld
metrics:
- accuracy
model-index:
- name: distilbert_lda_v1_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: 0.731461833901348
---

<!-- 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. -->

# distilbert_lda_v1_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: 3.1051
- Accuracy: 0.7315

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


### Framework versions

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