---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L10_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: 0.14372973477498716
---

<!-- 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_L10_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: 6.2119
- Accuracy: 0.1437

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.7198        | 0.14  | 10000  | 7.6446          | 0.0636   |
| 6.9424        | 0.27  | 20000  | 6.9379          | 0.0825   |
| 6.7559        | 0.41  | 30000  | 6.7492          | 0.0974   |
| 6.6513        | 0.55  | 40000  | 6.6455          | 0.1047   |
| 6.5706        | 0.68  | 50000  | 6.5734          | 0.1113   |
| 6.5236        | 0.82  | 60000  | 6.5176          | 0.1170   |
| 6.479         | 0.96  | 70000  | 6.4744          | 0.1212   |
| 6.4372        | 1.09  | 80000  | 6.4382          | 0.1255   |
| 6.4069        | 1.23  | 90000  | 6.4071          | 0.1275   |
| 6.3918        | 1.37  | 100000 | 6.3806          | 0.1302   |
| 6.3625        | 1.5   | 110000 | 6.3581          | 0.1320   |
| 6.339         | 1.64  | 120000 | 6.3400          | 0.1341   |
| 6.3189        | 1.78  | 130000 | 6.3237          | 0.1350   |
| 6.3115        | 1.91  | 140000 | 6.3103          | 0.1366   |
| 6.298         | 2.05  | 150000 | 6.2982          | 0.1374   |
| 6.2888        | 2.18  | 160000 | 6.2878          | 0.1382   |
| 6.2786        | 2.32  | 170000 | 6.2778          | 0.1392   |
| 6.2691        | 2.46  | 180000 | 6.2703          | 0.1398   |
| 6.2675        | 2.59  | 190000 | 6.2622          | 0.1402   |
| 6.255         | 2.73  | 200000 | 6.2571          | 0.1404   |
| 6.2481        | 2.87  | 210000 | 6.2517          | 0.1407   |
| 6.2465        | 3.0   | 220000 | 6.2447          | 0.1417   |
| 6.2381        | 3.14  | 230000 | 6.2399          | 0.1419   |
| 6.2332        | 3.28  | 240000 | 6.2371          | 0.1422   |
| 6.2279        | 3.41  | 250000 | 6.2321          | 0.1424   |
| 6.2275        | 3.55  | 260000 | 6.2274          | 0.1427   |
| 6.2186        | 3.69  | 270000 | 6.2249          | 0.1429   |
| 6.2192        | 3.82  | 280000 | 6.2231          | 0.1428   |
| 6.2228        | 3.96  | 290000 | 6.2208          | 0.1433   |
| 6.2154        | 4.1   | 300000 | 6.2192          | 0.1434   |
| 6.2204        | 4.23  | 310000 | 6.2177          | 0.1433   |
| 6.2123        | 4.37  | 320000 | 6.2162          | 0.1434   |
| 6.2136        | 4.51  | 330000 | 6.2134          | 0.1437   |
| 6.214         | 4.64  | 340000 | 6.2131          | 0.1439   |
| 6.2139        | 4.78  | 350000 | 6.2129          | 0.1437   |
| 6.211         | 4.92  | 360000 | 6.2130          | 0.1439   |


### Framework versions

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