recommendation-news-clicked-random-select-and-filter

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

• Loss: 0.5736
• Accuracy: 0.7001
• Macro F1: 0.6446

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
0.6446	0.0224	200	0.6369	0.6658	0.3997
0.65	0.0448	400	0.6337	0.6658	0.3997
0.6092	0.0672	600	0.6087	0.6725	0.5938
0.5988	0.0895	800	0.5995	0.6872	0.5664
0.5839	0.1119	1000	0.5979	0.6937	0.5908
0.6082	0.1343	1200	0.5879	0.6936	0.6149
0.5912	0.1567	1400	0.5857	0.6946	0.5626
0.5641	0.1791	1600	0.5848	0.6995	0.5927
0.5884	0.2015	1800	0.5797	0.6993	0.6093
0.5814	0.2239	2000	0.5807	0.6997	0.6100
0.5875	0.2462	2200	0.5774	0.7015	0.6151
0.5627	0.2686	2400	0.5796	0.6997	0.6302
0.5521	0.2910	2600	0.5856	0.7010	0.6140
0.5979	0.3134	2800	0.5742	0.7023	0.6094
0.6046	0.3358	3000	0.5792	0.6946	0.6408
0.5741	0.3582	3200	0.5781	0.7011	0.6301
0.566	0.3805	3400	0.5752	0.7013	0.6330
0.5589	0.4029	3600	0.5769	0.7010	0.6291
0.5758	0.4253	3800	0.5733	0.7033	0.6329
0.5714	0.4477	4000	0.5718	0.7044	0.6223
0.5797	0.4701	4200	0.5764	0.7021	0.6367
0.5669	0.4925	4400	0.5726	0.7022	0.6393
0.5655	0.5149	4600	0.5764	0.7062	0.6183
0.5743	0.5372	4800	0.5720	0.7053	0.6294
0.5657	0.5596	5000	0.5704	0.7047	0.6338
0.5766	0.5820	5200	0.5723	0.7031	0.6400
0.5748	0.6044	5400	0.5699	0.7067	0.6121
0.5669	0.6268	5600	0.5720	0.7048	0.6379
0.5557	0.6492	5800	0.5670	0.7071	0.6124
0.5675	0.6716	6000	0.5680	0.7075	0.6181
0.5808	0.6939	6200	0.5700	0.7066	0.6331
0.5792	0.7163	6400	0.5736	0.7001	0.6446
0.5583	0.7387	6600	0.5687	0.7060	0.6346
0.582	0.7611	6800	0.5667	0.7076	0.6248
0.5769	0.7835	7000	0.5694	0.7051	0.6411
0.568	0.8059	7200	0.5675	0.7081	0.6286
0.5712	0.8283	7400	0.5674	0.7084	0.6249
0.554	0.8506	7600	0.5675	0.7076	0.6350
0.5707	0.8730	7800	0.5661	0.7077	0.6347
0.577	0.8954	8000	0.5685	0.7066	0.6406
0.5766	0.9178	8200	0.5677	0.7077	0.6351
0.5992	0.9402	8400	0.5656	0.7084	0.6327
0.5744	0.9626	8600	0.5671	0.7061	0.6407
0.5748	0.9849	8800	0.5663	0.7078	0.6362

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1