---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L10_H768_A12
  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: 0.3705453911691882
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# HBERTv1_emb_compress_48_L10_H768_A12

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: 4.1748
- Accuracy: 0.3705

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.1074        | 0.08  | 10000  | 7.0838          | 0.0828   |
| 6.6784        | 0.16  | 20000  | 6.6795          | 0.1075   |
| 6.535         | 0.25  | 30000  | 6.5322          | 0.1192   |
| 6.4482        | 0.33  | 40000  | 6.4390          | 0.1267   |
| 6.3716        | 0.41  | 50000  | 6.3711          | 0.1324   |
| 6.3233        | 0.49  | 60000  | 6.3219          | 0.1351   |
| 6.2821        | 0.57  | 70000  | 6.2781          | 0.1383   |
| 6.251         | 0.66  | 80000  | 6.2431          | 0.1408   |
| 6.2159        | 0.74  | 90000  | 6.2111          | 0.1425   |
| 6.1838        | 0.82  | 100000 | 6.1774          | 0.1444   |
| 6.1338        | 0.9   | 110000 | 6.1349          | 0.1464   |
| 6.1022        | 0.98  | 120000 | 6.0939          | 0.1481   |
| 6.0194        | 1.07  | 130000 | 6.0080          | 0.1517   |
| 5.9309        | 1.15  | 140000 | 5.9199          | 0.1642   |
| 5.8593        | 1.23  | 150000 | 5.8326          | 0.1769   |
| 5.7093        | 1.31  | 160000 | 5.6659          | 0.2040   |
| 5.5018        | 1.39  | 170000 | 5.4433          | 0.2339   |
| 5.3036        | 1.47  | 180000 | 5.2292          | 0.2576   |
| 5.0629        | 1.56  | 190000 | 4.9895          | 0.2834   |
| 4.8311        | 1.64  | 200000 | 4.7638          | 0.3085   |
| 4.6239        | 1.72  | 210000 | 4.5799          | 0.3278   |
| 4.4305        | 1.8   | 220000 | 4.3821          | 0.3471   |
| 4.2209        | 1.88  | 230000 | 4.1749          | 0.3704   |


### Framework versions

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