scenario-KD-SCR-MSV-EN-CL-D2_data-en-massive_all_1_155

This model is a fine-tuned version of haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1 on the massive dataset. It achieves the following results on the evaluation set:

• Loss: nan
• Accuracy: 0.0315
• F1: 0.0010

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: 55
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.28	100	nan	0.0315	0.0010
No log	0.56	200	nan	0.0315	0.0010
No log	0.83	300	nan	0.0315	0.0010
No log	1.11	400	nan	0.0315	0.0010
2.4294	1.39	500	nan	0.0315	0.0010
2.4294	1.67	600	nan	0.0315	0.0010
2.4294	1.94	700	nan	0.0315	0.0010
2.4294	2.22	800	nan	0.0315	0.0010
2.4294	2.5	900	nan	0.0315	0.0010
0.0	2.78	1000	nan	0.0315	0.0010
0.0	3.06	1100	nan	0.0315	0.0010
0.0	3.33	1200	nan	0.0315	0.0010
0.0	3.61	1300	nan	0.0315	0.0010
0.0	3.89	1400	nan	0.0315	0.0010
0.0	4.17	1500	nan	0.0315	0.0010
0.0	4.44	1600	nan	0.0315	0.0010
0.0	4.72	1700	nan	0.0315	0.0010
0.0	5.0	1800	nan	0.0315	0.0010
0.0	5.28	1900	nan	0.0315	0.0010
0.0	5.56	2000	nan	0.0315	0.0010
0.0	5.83	2100	nan	0.0315	0.0010
0.0	6.11	2200	nan	0.0315	0.0010
0.0	6.39	2300	nan	0.0315	0.0010
0.0	6.67	2400	nan	0.0315	0.0010
0.0	6.94	2500	nan	0.0315	0.0010
0.0	7.22	2600	nan	0.0315	0.0010
0.0	7.5	2700	nan	0.0315	0.0010
0.0	7.78	2800	nan	0.0315	0.0010
0.0	8.06	2900	nan	0.0315	0.0010
0.0	8.33	3000	nan	0.0315	0.0010
0.0	8.61	3100	nan	0.0315	0.0010
0.0	8.89	3200	nan	0.0315	0.0010
0.0	9.17	3300	nan	0.0315	0.0010
0.0	9.44	3400	nan	0.0315	0.0010
0.0	9.72	3500	nan	0.0315	0.0010
0.0	10.0	3600	nan	0.0315	0.0010
0.0	10.28	3700	nan	0.0315	0.0010
0.0	10.56	3800	nan	0.0315	0.0010
0.0	10.83	3900	nan	0.0315	0.0010
0.0	11.11	4000	nan	0.0315	0.0010
0.0	11.39	4100	nan	0.0315	0.0010
0.0	11.67	4200	nan	0.0315	0.0010
0.0	11.94	4300	nan	0.0315	0.0010
0.0	12.22	4400	nan	0.0315	0.0010
0.0	12.5	4500	nan	0.0315	0.0010
0.0	12.78	4600	nan	0.0315	0.0010
0.0	13.06	4700	nan	0.0315	0.0010
0.0	13.33	4800	nan	0.0315	0.0010
0.0	13.61	4900	nan	0.0315	0.0010
0.0	13.89	5000	nan	0.0315	0.0010
0.0	14.17	5100	nan	0.0315	0.0010
0.0	14.44	5200	nan	0.0315	0.0010
0.0	14.72	5300	nan	0.0315	0.0010
0.0	15.0	5400	nan	0.0315	0.0010
0.0	15.28	5500	nan	0.0315	0.0010
0.0	15.56	5600	nan	0.0315	0.0010
0.0	15.83	5700	nan	0.0315	0.0010
0.0	16.11	5800	nan	0.0315	0.0010
0.0	16.39	5900	nan	0.0315	0.0010
0.0	16.67	6000	nan	0.0315	0.0010
0.0	16.94	6100	nan	0.0315	0.0010
0.0	17.22	6200	nan	0.0315	0.0010
0.0	17.5	6300	nan	0.0315	0.0010
0.0	17.78	6400	nan	0.0315	0.0010
0.0	18.06	6500	nan	0.0315	0.0010
0.0	18.33	6600	nan	0.0315	0.0010
0.0	18.61	6700	nan	0.0315	0.0010
0.0	18.89	6800	nan	0.0315	0.0010
0.0	19.17	6900	nan	0.0315	0.0010
0.0	19.44	7000	nan	0.0315	0.0010
0.0	19.72	7100	nan	0.0315	0.0010
0.0	20.0	7200	nan	0.0315	0.0010
0.0	20.28	7300	nan	0.0315	0.0010
0.0	20.56	7400	nan	0.0315	0.0010
0.0	20.83	7500	nan	0.0315	0.0010
0.0	21.11	7600	nan	0.0315	0.0010
0.0	21.39	7700	nan	0.0315	0.0010
0.0	21.67	7800	nan	0.0315	0.0010
0.0	21.94	7900	nan	0.0315	0.0010
0.0	22.22	8000	nan	0.0315	0.0010
0.0	22.5	8100	nan	0.0315	0.0010
0.0	22.78	8200	nan	0.0315	0.0010
0.0	23.06	8300	nan	0.0315	0.0010
0.0	23.33	8400	nan	0.0315	0.0010
0.0	23.61	8500	nan	0.0315	0.0010
0.0	23.89	8600	nan	0.0315	0.0010
0.0	24.17	8700	nan	0.0315	0.0010
0.0	24.44	8800	nan	0.0315	0.0010
0.0	24.72	8900	nan	0.0315	0.0010
0.0	25.0	9000	nan	0.0315	0.0010
0.0	25.28	9100	nan	0.0315	0.0010
0.0	25.56	9200	nan	0.0315	0.0010
0.0	25.83	9300	nan	0.0315	0.0010
0.0	26.11	9400	nan	0.0315	0.0010
0.0	26.39	9500	nan	0.0315	0.0010
0.0	26.67	9600	nan	0.0315	0.0010
0.0	26.94	9700	nan	0.0315	0.0010
0.0	27.22	9800	nan	0.0315	0.0010
0.0	27.5	9900	nan	0.0315	0.0010
0.0	27.78	10000	nan	0.0315	0.0010
0.0	28.06	10100	nan	0.0315	0.0010
0.0	28.33	10200	nan	0.0315	0.0010
0.0	28.61	10300	nan	0.0315	0.0010
0.0	28.89	10400	nan	0.0315	0.0010
0.0	29.17	10500	nan	0.0315	0.0010
0.0	29.44	10600	nan	0.0315	0.0010
0.0	29.72	10700	nan	0.0315	0.0010
0.0	30.0	10800	nan	0.0315	0.0010

Framework versions

• Transformers 4.33.3
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3