---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L12_H64_A2
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
      type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.12850906143802152
---

<!-- 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. -->

# HBERTv1_emb_compress_48_L12_H64_A2

This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4079
- Accuracy: 0.1285

## 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: 1e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 8.6554        | 0.16  | 10000  | 8.5846          | 0.0483   |
| 7.2331        | 0.33  | 20000  | 7.2280          | 0.0542   |
| 7.0014        | 0.49  | 30000  | 6.9927          | 0.0677   |
| 6.8699        | 0.66  | 40000  | 6.8637          | 0.0856   |
| 6.7777        | 0.82  | 50000  | 6.7726          | 0.0922   |
| 6.7091        | 0.98  | 60000  | 6.7101          | 0.0974   |
| 6.6626        | 1.15  | 70000  | 6.6620          | 0.1015   |
| 6.6279        | 1.31  | 80000  | 6.6255          | 0.1040   |
| 6.5917        | 1.47  | 90000  | 6.5948          | 0.1068   |
| 6.5691        | 1.64  | 100000 | 6.5695          | 0.1094   |
| 6.5486        | 1.8   | 110000 | 6.5460          | 0.1122   |
| 6.5246        | 1.97  | 120000 | 6.5275          | 0.1144   |
| 6.5069        | 2.13  | 130000 | 6.5115          | 0.1162   |
| 6.5001        | 2.29  | 140000 | 6.4962          | 0.1180   |
| 6.4785        | 2.46  | 150000 | 6.4822          | 0.1197   |
| 6.4706        | 2.62  | 160000 | 6.4714          | 0.1212   |
| 6.4612        | 2.79  | 170000 | 6.4610          | 0.1225   |
| 6.4485        | 2.95  | 180000 | 6.4530          | 0.1233   |
| 6.4477        | 3.11  | 190000 | 6.4441          | 0.1243   |
| 6.4373        | 3.28  | 200000 | 6.4395          | 0.1251   |
| 6.4351        | 3.44  | 210000 | 6.4322          | 0.1259   |
| 6.4273        | 3.6   | 220000 | 6.4264          | 0.1262   |
| 6.4153        | 3.77  | 230000 | 6.4219          | 0.1269   |
| 6.4188        | 3.93  | 240000 | 6.4182          | 0.1274   |
| 6.4128        | 4.1   | 250000 | 6.4150          | 0.1278   |
| 6.4189        | 4.26  | 260000 | 6.4121          | 0.1280   |
| 6.4102        | 4.42  | 270000 | 6.4112          | 0.1282   |
| 6.4105        | 4.59  | 280000 | 6.4087          | 0.1285   |
| 6.4065        | 4.75  | 290000 | 6.4067          | 0.1287   |
| 6.4082        | 4.92  | 300000 | 6.4070          | 0.1285   |


### Framework versions

- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3