bert_12_layer_model_v1_complete_training_new_wt_init_96
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_72 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2871
- Accuracy: 0.5730
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 |
---|---|---|---|---|
2.5078 | 0.08 | 10000 | 2.4110 | 0.5558 |
2.4913 | 0.16 | 20000 | 2.3958 | 0.5579 |
2.4725 | 0.25 | 30000 | 2.3794 | 0.5603 |
2.4698 | 0.33 | 40000 | 2.3644 | 0.5623 |
2.4431 | 0.41 | 50000 | 2.3489 | 0.5645 |
2.4345 | 0.49 | 60000 | 2.3352 | 0.5665 |
2.412 | 0.57 | 70000 | 2.3221 | 0.5683 |
2.3999 | 0.66 | 80000 | 2.3079 | 0.5697 |
2.3844 | 0.74 | 90000 | 2.2933 | 0.5713 |
2.3732 | 0.82 | 100000 | 2.2871 | 0.5730 |
Framework versions
- Transformers 4.30.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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