MLMA_lab9_task2

This model is a fine-tuned version of microsoft/biogpt on the ncbi_disease dataset. It achieves the following results on the evaluation set:

• Loss: 1.2509
• Precision: 0.0159
• Recall: 0.1487
• F1: 0.0287
• Accuracy: 0.6365

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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.153	1.0	680	1.0671	0.0122	0.1258	0.0223	0.5452
1.02	2.0	1360	1.0418	0.0098	0.0203	0.0132	0.6791
0.9552	3.0	2040	1.0269	0.0135	0.1677	0.0250	0.5282
0.926	4.0	2720	1.0390	0.0143	0.0940	0.0248	0.6686
0.9156	5.0	3400	1.0200	0.0135	0.2046	0.0253	0.4679
0.8791	6.0	4080	1.0543	0.0131	0.2745	0.0250	0.3149
0.8672	7.0	4760	1.0545	0.0141	0.2732	0.0267	0.3471
0.8627	8.0	5440	1.0734	0.0145	0.0826	0.0246	0.7220
0.8375	9.0	6120	1.1068	0.0156	0.1410	0.0281	0.6451
0.8235	10.0	6800	1.0796	0.0158	0.1537	0.0286	0.6210
0.8157	11.0	7480	1.1476	0.0143	0.1690	0.0263	0.5737
0.7957	12.0	8160	1.1369	0.0143	0.1525	0.0262	0.6155
0.7937	13.0	8840	1.2014	0.0151	0.1741	0.0278	0.5808
0.7765	14.0	9520	1.2249	0.0160	0.1449	0.0289	0.6443
0.7661	15.0	10200	1.2509	0.0159	0.1487	0.0287	0.6365

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3