---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: bert_12_layer_model_v3_complete_training_new_emb_compress_48
  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: 0.1573752894874488
---

<!-- 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_12_layer_model_v3_complete_training_new_emb_compress_48

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: 5.9594
- Accuracy: 0.1574

## 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: 48
- eval_batch_size: 48
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.1148        | 0.08  | 10000  | 7.0921          | 0.0828   |
| 6.6864        | 0.16  | 20000  | 6.6879          | 0.1078   |
| 6.5451        | 0.25  | 30000  | 6.5435          | 0.1184   |
| 6.4606        | 0.33  | 40000  | 6.4515          | 0.1262   |
| 6.3851        | 0.41  | 50000  | 6.3851          | 0.1312   |
| 6.3371        | 0.49  | 60000  | 6.3357          | 0.1342   |
| 6.2971        | 0.57  | 70000  | 6.2923          | 0.1373   |
| 6.2682        | 0.66  | 80000  | 6.2605          | 0.1399   |
| 6.2352        | 0.74  | 90000  | 6.2301          | 0.1411   |
| 6.214         | 0.82  | 100000 | 6.2090          | 0.1430   |
| 6.1837        | 0.9   | 110000 | 6.1865          | 0.1443   |
| 6.1726        | 0.98  | 120000 | 6.1682          | 0.1451   |
| 6.1524        | 1.07  | 130000 | 6.1498          | 0.1464   |
| 6.1293        | 1.15  | 140000 | 6.1300          | 0.1468   |
| 6.1116        | 1.23  | 150000 | 6.1026          | 0.1479   |
| 6.0839        | 1.31  | 160000 | 6.0797          | 0.1490   |
| 6.0616        | 1.39  | 170000 | 6.0590          | 0.1499   |
| 6.0508        | 1.47  | 180000 | 6.0399          | 0.1509   |
| 6.0311        | 1.56  | 190000 | 6.0233          | 0.1517   |
| 6.015         | 1.64  | 200000 | 6.0048          | 0.1533   |
| 5.985         | 1.72  | 210000 | 5.9863          | 0.1547   |
| 5.9661        | 1.8   | 220000 | 5.9595          | 0.1573   |


### Framework versions

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