FNST_trad_j

This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.6540
• Accuracy: 0.6525
• F1: 0.6178

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: 1e-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
• num_epochs: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.1058	1.0	1500	1.0564	0.5442	0.3843
0.9559	2.0	3000	0.9522	0.585	0.5503
0.8789	3.0	4500	0.8843	0.61	0.5733
0.8292	4.0	6000	0.8614	0.6167	0.5734
0.7807	5.0	7500	0.8519	0.62	0.5896
0.7559	6.0	9000	0.8648	0.6283	0.5965
0.7098	7.0	10500	0.8579	0.63	0.5961
0.6703	8.0	12000	0.8536	0.6417	0.6029
0.6114	9.0	13500	0.8686	0.6358	0.5997
0.611	10.0	15000	0.8948	0.6342	0.6045
0.5614	11.0	16500	0.9173	0.6342	0.6046
0.515	12.0	18000	0.9289	0.6425	0.6089
0.5107	13.0	19500	0.9581	0.64	0.6052
0.4691	14.0	21000	1.0099	0.6433	0.6091
0.4476	15.0	22500	1.0543	0.6458	0.6108
0.398	16.0	24000	1.1170	0.6425	0.6051
0.3828	17.0	25500	1.1585	0.6517	0.6102
0.3567	18.0	27000	1.2252	0.6475	0.6114
0.3334	19.0	28500	1.2827	0.6675	0.6317
0.2982	20.0	30000	1.4256	0.6517	0.6257
0.2734	21.0	31500	1.4591	0.6583	0.6305
0.2556	22.0	33000	1.5516	0.66	0.6263
0.2409	23.0	34500	1.6793	0.6592	0.6219
0.2226	24.0	36000	1.8157	0.66	0.6218
0.1971	25.0	37500	1.9089	0.6575	0.6241
0.1832	26.0	39000	2.0406	0.6558	0.6300
0.1921	27.0	40500	2.1448	0.6583	0.6254
0.1496	28.0	42000	2.2888	0.6458	0.6136
0.1538	29.0	43500	2.3520	0.66	0.6241
0.1558	30.0	45000	2.4748	0.6492	0.6207
0.1409	31.0	46500	2.5126	0.6542	0.6175
0.119	32.0	48000	2.6540	0.6525	0.6178

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1