albert-base-v2_mnli_bc

This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.2952
• Accuracy: 0.9399

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2159	1.0	16363	0.2268	0.9248
0.1817	2.0	32726	0.2335	0.9347
0.0863	3.0	49089	0.3014	0.9401

Framework versions

• Transformers 4.13.0
• Pytorch 1.10.1+cu111
• Datasets 1.17.0
• Tokenizers 0.10.3