deberta-v3-base-cotat

This model is a fine-tuned version of microsoft/deberta-v3-base on the DandinPower/review_cleanonlytitleandtext dataset. It achieves the following results on the evaluation set:

• Loss: 1.4985
• Accuracy: 0.623
• Macro F1: 0.6247

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: 4.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1500
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
1.0223	0.14	500	0.9610	0.592	0.5971
1.0108	0.29	1000	0.9378	0.6044	0.6083
0.9323	0.43	1500	0.9605	0.589	0.5652
0.9651	0.57	2000	0.9845	0.5797	0.5687
0.928	0.71	2500	0.9521	0.5907	0.5656
0.9205	0.86	3000	0.9073	0.603	0.5740
0.9243	1.0	3500	0.8876	0.616	0.6113
0.8545	1.14	4000	0.8631	0.6267	0.6290
0.8267	1.29	4500	0.8908	0.624	0.6185
0.8175	1.43	5000	0.8771	0.6173	0.6222
0.8613	1.57	5500	0.9564	0.6209	0.6081
0.8138	1.71	6000	0.9246	0.6089	0.6063
0.7314	1.86	6500	0.9030	0.6329	0.6313
0.8287	2.0	7000	0.8753	0.6211	0.6235
0.6963	2.14	7500	0.9700	0.6247	0.6257
0.7034	2.29	8000	0.9592	0.6234	0.6220
0.679	2.43	8500	0.8994	0.6233	0.6272
0.7207	2.57	9000	1.0013	0.6236	0.6183
0.6992	2.71	9500	0.9385	0.6169	0.6219
0.7032	2.86	10000	0.9247	0.6366	0.6364
0.6949	3.0	10500	0.9615	0.6239	0.6281
0.5581	3.14	11000	1.0439	0.6217	0.6267
0.55	3.29	11500	1.1205	0.6259	0.6232
0.5496	3.43	12000	1.1122	0.6226	0.6267
0.5462	3.57	12500	1.0692	0.6251	0.6263
0.5121	3.71	13000	1.1563	0.6197	0.6214
0.531	3.86	13500	1.1123	0.6261	0.6256
0.5256	4.0	14000	1.1194	0.6247	0.6264
0.3908	4.14	14500	1.3631	0.6204	0.6210
0.4439	4.29	15000	1.4810	0.6204	0.6211
0.4252	4.43	15500	1.4454	0.6211	0.6217
0.3721	4.57	16000	1.5315	0.6204	0.6231
0.369	4.71	16500	1.4797	0.6184	0.6190
0.3907	4.86	17000	1.4857	0.6219	0.6234
0.4022	5.0	17500	1.4985	0.623	0.6247

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2