electra-base-discriminator-CoLA

This model is a fine-tuned version of google/electra-base-discriminator on the GLUE COLA dataset. It achieves the following results on the evaluation set:

• Loss: 0.3542
• Matthews Correlation: 0.6580

Model description

Trying to find a decent optimum between accuracy/quality and inference speed.

{
    "epoch": 8.0,
    "eval_loss": 0.3541961908340454,
    "eval_matthews_correlation": 0.6579677841732349,
    "eval_runtime": 1.9552,
    "eval_samples": 1043,
    "eval_samples_per_second": 533.451,
    "eval_steps_per_second": 33.756
}

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: 8e-05
• train_batch_size: 128
• eval_batch_size: 16
• seed: 22165
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 8.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Matthews Correlation
0.4004	1.0	67	0.3569	0.6340
0.2843	2.0	134	0.3542	0.6580
0.1228	3.0	201	0.4201	0.6412
0.0989	4.0	268	0.4780	0.6757
0.0681	5.0	335	0.4900	0.6925
0.0506	6.0	402	0.5837	0.6785
0.0093	7.0	469	0.6298	0.6652
0.0244	8.0	536	0.6292	0.6750

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.1