classifier-python-clip

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

• Loss: 0.3986
• Precision: 0.6276
• Recall: 0.4552
• F1 Macro: 0.4790
• Accuracy: 0.5751
• F1 Binary Minimum3: 0.6760

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: 0.0001
• train_batch_size: 16
• eval_batch_size: 256
• seed: 0
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 128
• total_eval_batch_size: 2048
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 Macro	Accuracy	F1 Binary Minimum3
No log	0	0	8.1813	0.0397	0.25	0.0685	0.1589	0
0.4365	1.4245	1000	0.4329	0.5891	0.4124	0.4144	0.5610	0.6330
0.425	2.8490	2000	0.4181	0.5889	0.4235	0.4329	0.5650	0.6545
0.4193	4.2735	3000	0.4195	0.6160	0.4435	0.4618	0.5594	0.6854
0.4023	5.6980	4000	0.4109	0.5988	0.4371	0.4499	0.5689	0.6590
0.4116	7.1225	5000	0.4074	0.6116	0.4439	0.4611	0.5727	0.6670
0.4148	8.5470	6000	0.4137	0.5996	0.4373	0.4488	0.5716	0.6478
0.4107	9.9715	7000	0.4078	0.6011	0.4407	0.4537	0.5710	0.6598
0.4104	11.3960	8000	0.4036	0.6133	0.4474	0.4656	0.5739	0.6720
0.4033	12.8205	9000	0.4044	0.6054	0.4406	0.4571	0.5703	0.6640
0.3945	14.2450	10000	0.4029	0.6258	0.4502	0.4720	0.5737	0.6782
0.4052	15.6695	11000	0.4038	0.6159	0.4522	0.4699	0.5770	0.6694
0.3976	17.0940	12000	0.4054	0.6143	0.4398	0.4531	0.5758	0.6572
0.3996	18.5185	13000	0.4015	0.6186	0.4514	0.4725	0.5722	0.6794
0.4008	19.9430	14000	0.4071	0.6335	0.4475	0.4704	0.5640	0.6833
0.4047	21.3675	15000	0.4024	0.6114	0.4466	0.4671	0.5699	0.6737
0.4075	22.7920	16000	0.4029	0.6268	0.4486	0.4714	0.5697	0.6806
0.403	24.2165	17000	0.4014	0.6225	0.4552	0.4787	0.5709	0.6794
0.4027	25.6410	18000	0.4007	0.6238	0.4519	0.4751	0.5733	0.6788
0.399	27.0655	19000	0.4005	0.6159	0.4529	0.4755	0.5736	0.6743
0.401	28.4900	20000	0.4013	0.6239	0.4565	0.4793	0.5713	0.6827
0.4008	29.9145	21000	0.4004	0.6150	0.4534	0.4731	0.5760	0.6707
0.4017	31.3390	22000	0.3997	0.6232	0.4557	0.4796	0.5756	0.6779
0.4028	32.7635	23000	0.3996	0.6272	0.4504	0.4722	0.5722	0.6775
0.4019	34.1880	24000	0.3993	0.6176	0.4500	0.4706	0.5748	0.6727
0.3947	35.6125	25000	0.3994	0.6149	0.4466	0.4648	0.5749	0.6701
0.4106	37.0370	26000	0.4043	0.6295	0.4635	0.4890	0.5702	0.6862
0.3907	38.4615	27000	0.3991	0.6172	0.4540	0.4772	0.5747	0.6747
0.3947	39.8860	28000	0.3996	0.6223	0.4556	0.4794	0.5736	0.6792
0.3934	41.3105	29000	0.3988	0.6244	0.4486	0.4713	0.5749	0.6719
0.3808	42.7350	30000	0.4016	0.6295	0.4591	0.4844	0.5723	0.6853
0.3898	44.1595	31000	0.3988	0.6171	0.4504	0.4704	0.5749	0.6723
0.3928	45.5840	32000	0.3987	0.6318	0.4560	0.4798	0.5758	0.6792
0.4044	47.0085	33000	0.3985	0.6241	0.4555	0.4792	0.5751	0.6757
0.4076	48.4330	34000	0.4000	0.6278	0.4588	0.4839	0.5738	0.6834
0.3923	49.8575	35000	0.3986	0.6276	0.4552	0.4790	0.5751	0.6760

Framework versions

• Transformers 4.43.4
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1