convnext-tiny-224_finetuned_on_unlabelled_IA_with_snorkel_labels

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

• Loss: 0.4381
• Precision: 0.8239
• Recall: 0.7919
• F1: 0.8058
• Accuracy: 0.8629

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: 0.001
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	112	0.5589	0.7547	0.5380	0.5097	0.7679
No log	2.0	224	0.5578	0.7691	0.5387	0.5103	0.7690
No log	3.0	336	0.4812	0.8513	0.7371	0.7709	0.8555
No log	4.0	448	0.4387	0.8734	0.6539	0.6835	0.8259
0.482	5.0	560	0.4427	0.8322	0.6250	0.6449	0.8085
0.482	6.0	672	0.6234	0.8219	0.5702	0.5635	0.7848
0.482	7.0	784	0.6187	0.8791	0.6070	0.6196	0.8054
0.482	8.0	896	0.3953	0.8683	0.7134	0.7507	0.8502
0.3656	9.0	1008	0.4381	0.8239	0.7919	0.8058	0.8629
0.3656	10.0	1120	0.5346	0.7794	0.7900	0.7844	0.8370
0.3656	11.0	1232	0.3685	0.8678	0.7600	0.7943	0.8681
0.3656	12.0	1344	0.6900	0.6244	0.6667	0.6099	0.6435
0.3656	13.0	1456	0.6097	0.6832	0.7149	0.6931	0.7511
0.2987	14.0	1568	0.5435	0.8746	0.6754	0.7096	0.8354
0.2987	15.0	1680	0.5525	0.7277	0.7690	0.7411	0.7890
0.2987	16.0	1792	0.5003	0.8086	0.7694	0.7856	0.8507
0.2987	17.0	1904	0.8172	0.6183	0.6576	0.6074	0.6450
0.2598	18.0	2016	0.6102	0.6977	0.7489	0.7070	0.75
0.2598	19.0	2128	0.4260	0.8523	0.7497	0.7822	0.8602
0.2598	20.0	2240	0.5503	0.8276	0.6770	0.7079	0.8281
0.2598	21.0	2352	0.4574	0.7994	0.7785	0.7879	0.8481
0.2598	22.0	2464	0.6307	0.8620	0.6353	0.6592	0.8165
0.2111	23.0	2576	0.4605	0.8196	0.7697	0.7894	0.8555
0.2111	24.0	2688	0.5290	0.8152	0.7320	0.7592	0.8434
0.2111	25.0	2800	0.4754	0.8755	0.7216	0.7599	0.8550
0.2111	26.0	2912	0.5161	0.8428	0.7436	0.7750	0.8555
0.1638	27.0	3024	0.5753	0.7358	0.7278	0.7316	0.8043
0.1638	28.0	3136	0.6403	0.8468	0.7016	0.7360	0.8412
0.1638	29.0	3248	0.5418	0.7912	0.7473	0.7647	0.8381
0.1638	30.0	3360	0.5651	0.8240	0.7315	0.7607	0.8460

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.0
• Tokenizers 0.13.1