furina_seed42_eng_amh_esp

This model is a fine-tuned version of yihongLiu/furina on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0131
• Spearman Corr: 0.8481

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• 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	Spearman Corr
No log	0.59	200	0.0247	0.6933
No log	1.18	400	0.0227	0.7395
No log	1.76	600	0.0217	0.7604
0.0471	2.35	800	0.0192	0.7685
0.0471	2.94	1000	0.0186	0.7805
0.0471	3.53	1200	0.0178	0.7884
0.0196	4.12	1400	0.0179	0.7961
0.0196	4.71	1600	0.0177	0.7990
0.0196	5.29	1800	0.0178	0.8035
0.0196	5.88	2000	0.0164	0.8057
0.0145	6.47	2200	0.0167	0.8070
0.0145	7.06	2400	0.0159	0.8110
0.0145	7.65	2600	0.0165	0.8121
0.0106	8.24	2800	0.0161	0.8110
0.0106	8.82	3000	0.0159	0.8148
0.0106	9.41	3200	0.0155	0.8195
0.008	10.0	3400	0.0151	0.8227
0.008	10.59	3600	0.0149	0.8253
0.008	11.18	3800	0.0154	0.8244
0.008	11.76	4000	0.0147	0.8251
0.0064	12.35	4200	0.0148	0.8249
0.0064	12.94	4400	0.0149	0.8287
0.0064	13.53	4600	0.0147	0.8297
0.0052	14.12	4800	0.0142	0.8347
0.0052	14.71	5000	0.0148	0.8314
0.0052	15.29	5200	0.0141	0.8341
0.0052	15.88	5400	0.0139	0.8386
0.0045	16.47	5600	0.0139	0.8350
0.0045	17.06	5800	0.0137	0.8389
0.0045	17.65	6000	0.0136	0.8402
0.004	18.24	6200	0.0139	0.8400
0.004	18.82	6400	0.0138	0.8414
0.004	19.41	6600	0.0136	0.8433
0.0036	20.0	6800	0.0140	0.8420
0.0036	20.59	7000	0.0136	0.8434
0.0036	21.18	7200	0.0137	0.8451
0.0036	21.76	7400	0.0133	0.8445
0.0032	22.35	7600	0.0135	0.8451
0.0032	22.94	7800	0.0136	0.8447
0.0032	23.53	8000	0.0136	0.8449
0.003	24.12	8200	0.0132	0.8463
0.003	24.71	8400	0.0131	0.8472
0.003	25.29	8600	0.0133	0.8477
0.003	25.88	8800	0.0135	0.8472
0.0028	26.47	9000	0.0134	0.8478
0.0028	27.06	9200	0.0131	0.8477
0.0028	27.65	9400	0.0131	0.8478
0.0026	28.24	9600	0.0130	0.8482
0.0026	28.82	9800	0.0131	0.8479
0.0026	29.41	10000	0.0131	0.8481

Framework versions

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