multimodal-traj-class-no-numtransform

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1964
• Acc: 0.7237
• Relacc: 0.8446
• Num Fours: 617
• Mcc: 0.6029

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	Relacc	Num Fours	Mcc
0.9461	1.0	1212	0.8645	0.6752	0.7586	680	0.5115
0.8387	2.0	2424	0.7880	0.6979	0.7889	630	0.5526
0.7565	3.0	3636	0.7489	0.7183	0.8015	636	0.5851
0.6997	4.0	4848	0.7542	0.7061	0.7908	574	0.5569
0.6516	5.0	6060	0.6806	0.7388	0.8192	660	0.6176
0.6049	6.0	7272	0.6898	0.7406	0.8395	638	0.6269
0.5526	7.0	8484	0.6848	0.7408	0.8413	648	0.6288
0.5343	8.0	9696	0.6904	0.7359	0.8413	645	0.6207
0.4855	9.0	10908	0.7219	0.7400	0.8456	587	0.6253
0.4618	10.0	12120	0.7310	0.7464	0.8448	624	0.6314
0.4326	11.0	13332	0.7298	0.7575	0.8508	658	0.6536
0.4098	12.0	14544	0.8706	0.7266	0.8395	611	0.6026
0.3707	13.0	15756	0.8682	0.7431	0.8415	629	0.6260
0.3377	14.0	16968	0.9299	0.7371	0.8467	590	0.6220
0.315	15.0	18180	0.9393	0.7365	0.8463	635	0.6190
0.2984	16.0	19392	1.0106	0.7348	0.8426	593	0.6134
0.2804	17.0	20604	1.0719	0.7307	0.8465	623	0.6118
0.2644	18.0	21816	1.1245	0.7280	0.8446	642	0.6117
0.2469	19.0	23028	1.1745	0.7258	0.8430	619	0.6044
0.2273	20.0	24240	1.1964	0.7237	0.8446	617	0.6029

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.7.1
• Tokenizers 0.13.2