BERT_pretraining_h_100
This model is a fine-tuned version of bert-large-uncased on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 7.2715
- Accuracy: 0.0465
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: 0.0001
- train_batch_size: 36
- eval_batch_size: 36
- 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: 100000
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
6.5136 | 0.06 | 10000 | 6.4841 | 0.1332 |
6.3225 | 0.12 | 20000 | 6.2865 | 0.1452 |
6.0806 | 0.18 | 30000 | 6.1945 | 0.1482 |
6.1449 | 0.25 | 40000 | 6.1284 | 0.1497 |
5.7325 | 0.31 | 50000 | 5.8403 | 0.1609 |
4.0177 | 0.37 | 60000 | 3.7789 | 0.3887 |
3.3942 | 0.43 | 70000 | 3.1742 | 0.4638 |
3.2801 | 0.49 | 80000 | 3.0618 | 0.4775 |
7.2562 | 0.55 | 90000 | 7.2798 | 0.0432 |
7.226 | 0.61 | 100000 | 7.2771 | 0.0465 |
7.2174 | 0.68 | 110000 | 7.2764 | 0.0465 |
7.232 | 0.74 | 120000 | 7.2745 | 0.0465 |
7.2003 | 0.8 | 130000 | 7.2730 | 0.0465 |
7.0964 | 0.86 | 140000 | 7.2725 | 0.0466 |
7.5174 | 0.92 | 150000 | 7.2729 | 0.0465 |
7.2674 | 0.98 | 160000 | 7.2729 | 0.0465 |
7.2044 | 1.04 | 170000 | 7.2729 | 0.0466 |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Base model
google-bert/bert-large-uncasedDataset used to train gokuls/BERT_pretraining_h_100
Evaluation results
- Accuracy on gokuls/wiki_book_corpus_complete_processed_bert_datasetself-reported0.047