distilbert-base-uncased__subj__train-8-0

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4440
• Accuracy: 0.789

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7163	1.0	3	0.6868	0.5
0.6683	2.0	6	0.6804	0.75
0.6375	3.0	9	0.6702	0.75
0.5997	4.0	12	0.6686	0.75
0.5345	5.0	15	0.6720	0.75
0.4673	6.0	18	0.6646	0.75
0.4214	7.0	21	0.6494	0.75
0.3439	8.0	24	0.6313	0.75
0.3157	9.0	27	0.6052	0.75
0.2329	10.0	30	0.5908	0.75
0.1989	11.0	33	0.5768	0.75
0.1581	12.0	36	0.5727	0.75
0.1257	13.0	39	0.5678	0.75
0.1005	14.0	42	0.5518	0.75
0.0836	15.0	45	0.5411	0.75
0.0611	16.0	48	0.5320	0.75
0.0503	17.0	51	0.5299	0.75
0.0407	18.0	54	0.5368	0.75
0.0332	19.0	57	0.5455	0.75
0.0293	20.0	60	0.5525	0.75
0.0254	21.0	63	0.5560	0.75
0.0231	22.0	66	0.5569	0.75
0.0201	23.0	69	0.5572	0.75
0.0179	24.0	72	0.5575	0.75
0.0184	25.0	75	0.5547	0.75
0.0148	26.0	78	0.5493	0.75
0.0149	27.0	81	0.5473	0.75

Framework versions

• Transformers 4.15.0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2
• Tokenizers 0.10.3