hBERTv2_new_pretrain_w_init__mrpc
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.5908
- Accuracy: 0.7059
- F1: 0.8193
- Combined Score: 0.7626
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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
---|---|---|---|---|---|---|
0.6576 | 1.0 | 29 | 0.5908 | 0.7059 | 0.8193 | 0.7626 |
0.6172 | 2.0 | 58 | 0.6228 | 0.6495 | 0.7433 | 0.6964 |
0.5641 | 3.0 | 87 | 0.6026 | 0.6936 | 0.7780 | 0.7358 |
0.4682 | 4.0 | 116 | 0.6339 | 0.7034 | 0.7973 | 0.7504 |
0.3677 | 5.0 | 145 | 0.9408 | 0.6495 | 0.7307 | 0.6901 |
0.2183 | 6.0 | 174 | 0.8311 | 0.6544 | 0.7478 | 0.7011 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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Dataset used to train gokuls/hBERTv2_new_pretrain_w_init__mrpc
Evaluation results
- Accuracy on GLUE MRPCvalidation set self-reported0.706
- F1 on GLUE MRPCvalidation set self-reported0.819