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---
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
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