tiny-bert-mnli-distilled

It achieves the following results on the evaluation set:

• Loss: 1.5018
• Accuracy: 0.5819
• F1 score: 0.5782
• Precision score: 0.6036
• Metric recall: 0.5819

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: 0.0005
• train_batch_size: 64
• eval_batch_size: 32
• seed: 33
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 score	Precision score	Metric recall
1.4475	1.0	614	1.4296	0.4521	0.4070	0.5621	0.4521
1.3354	2.0	1228	1.4320	0.4805	0.4579	0.5276	0.4805
1.2244	3.0	1842	1.4786	0.5699	0.5602	0.5865	0.5699
1.1416	4.0	2456	1.5018	0.5819	0.5782	0.6036	0.5819

Framework versions

• Transformers 4.18.0
• Pytorch 1.9.1
• Datasets 2.1.0
• Tokenizers 0.11.6