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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_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
should probably proofread and complete it, then remove this comment. -->
# 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
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