---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L12_H128_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: 0.14404758839681311
---

<!-- 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_H128_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: 6.2072
- Accuracy: 0.1440

## 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: 80
- eval_batch_size: 80
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.7257        | 0.14  | 10000  | 7.6502          | 0.0520   |
| 6.9502        | 0.27  | 20000  | 6.9458          | 0.0829   |
| 6.7568        | 0.41  | 30000  | 6.7497          | 0.0973   |
| 6.6513        | 0.55  | 40000  | 6.6447          | 0.1047   |
| 6.5712        | 0.68  | 50000  | 6.5735          | 0.1112   |
| 6.5237        | 0.82  | 60000  | 6.5170          | 0.1165   |
| 6.478         | 0.96  | 70000  | 6.4726          | 0.1209   |
| 6.4359        | 1.09  | 80000  | 6.4369          | 0.1252   |
| 6.4052        | 1.23  | 90000  | 6.4047          | 0.1273   |
| 6.3897        | 1.37  | 100000 | 6.3794          | 0.1299   |
| 6.3598        | 1.5   | 110000 | 6.3557          | 0.1319   |
| 6.3362        | 1.64  | 120000 | 6.3374          | 0.1341   |
| 6.3154        | 1.78  | 130000 | 6.3209          | 0.1348   |
| 6.3082        | 1.91  | 140000 | 6.3069          | 0.1367   |
| 6.2942        | 2.05  | 150000 | 6.2943          | 0.1377   |
| 6.2849        | 2.18  | 160000 | 6.2835          | 0.1381   |
| 6.2745        | 2.32  | 170000 | 6.2737          | 0.1391   |
| 6.2647        | 2.46  | 180000 | 6.2658          | 0.1398   |
| 6.2633        | 2.59  | 190000 | 6.2580          | 0.1407   |
| 6.2506        | 2.73  | 200000 | 6.2525          | 0.1407   |
| 6.2435        | 2.87  | 210000 | 6.2463          | 0.1413   |
| 6.2416        | 3.0   | 220000 | 6.2394          | 0.1419   |
| 6.2329        | 3.14  | 230000 | 6.2355          | 0.1421   |
| 6.2288        | 3.28  | 240000 | 6.2323          | 0.1426   |
| 6.2232        | 3.41  | 250000 | 6.2277          | 0.1428   |
| 6.2227        | 3.55  | 260000 | 6.2228          | 0.1431   |
| 6.2138        | 3.69  | 270000 | 6.2200          | 0.1433   |
| 6.2142        | 3.82  | 280000 | 6.2187          | 0.1433   |
| 6.2182        | 3.96  | 290000 | 6.2162          | 0.1435   |
| 6.2108        | 4.1   | 300000 | 6.2145          | 0.1438   |
| 6.2158        | 4.23  | 310000 | 6.2131          | 0.1437   |
| 6.2072        | 4.37  | 320000 | 6.2114          | 0.1438   |
| 6.2084        | 4.51  | 330000 | 6.2087          | 0.1440   |
| 6.2093        | 4.64  | 340000 | 6.2082          | 0.1443   |
| 6.2084        | 4.78  | 350000 | 6.2081          | 0.1440   |
| 6.2066        | 4.92  | 360000 | 6.2081          | 0.1442   |


### Framework versions

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