S04

This model is a fine-tuned version of Anwaarma/Merged-Server-praj on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5249
• Accuracy: 0.71
• F1: 0.8304

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: 4e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.01	50	0.6296	0.65	0.6505
No log	0.01	100	0.6280	0.67	0.6697
No log	0.02	150	0.6210	0.67	0.6665
No log	0.03	200	0.6583	0.65	0.6505
No log	0.03	250	0.6650	0.65	0.6505
No log	0.04	300	0.6613	0.67	0.6697
No log	0.04	350	0.6663	0.65	0.6502
No log	0.05	400	0.6704	0.67	0.6702
No log	0.06	450	0.6570	0.68	0.6794
0.6123	0.06	500	0.6430	0.65	0.6502
0.6123	0.07	550	0.6558	0.64	0.6404
0.6123	0.08	600	0.6662	0.64	0.64
0.6123	0.08	650	0.6547	0.64	0.6406
0.6123	0.09	700	0.6407	0.66	0.6605
0.6123	0.09	750	0.6238	0.66	0.6605
0.6123	0.1	800	0.6223	0.68	0.6794
0.6123	0.11	850	0.6006	0.66	0.6604
0.6123	0.11	900	0.6294	0.68	0.6773
0.6123	0.12	950	0.6195	0.66	0.66
0.6014	0.13	1000	0.6119	0.65	0.6505
0.6014	0.13	1050	0.6230	0.67	0.6702
0.6014	0.14	1100	0.6410	0.69	0.6905
0.6014	0.14	1150	0.6306	0.67	0.6705
0.6014	0.15	1200	0.6476	0.7	0.6994
0.6014	0.16	1250	0.6244	0.67	0.6705
0.6014	0.16	1300	0.6078	0.69	0.6897
0.6014	0.17	1350	0.5869	0.67	0.6705
0.6014	0.18	1400	0.6164	0.67	0.6665
0.6014	0.18	1450	0.6054	0.65	0.6505
0.5906	0.19	1500	0.5947	0.67	0.6705
0.5906	0.19	1550	0.5765	0.69	0.6905
0.5906	0.2	1600	0.5677	0.69	0.6905
0.5906	0.21	1650	0.5828	0.7	0.7005
0.5906	0.21	1700	0.6249	0.67	0.6689
0.5906	0.22	1750	0.5833	0.69	0.6905
0.5906	0.23	1800	0.5838	0.68	0.6804
0.5906	0.23	1850	0.5923	0.7	0.7004
0.5906	0.24	1900	0.5749	0.69	0.6905
0.5906	0.25	1950	0.5769	0.7	0.7004
0.5736	0.25	2000	0.5706	0.7	0.7005
0.5736	0.26	2050	0.5967	0.69	0.6897
0.5736	0.26	2100	0.5866	0.69	0.6897
0.5736	0.27	2150	0.5901	0.7	0.7
0.5736	0.28	2200	0.5771	0.7	0.7004
0.5736	0.28	2250	0.5616	0.69	0.6905

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.0
• Tokenizers 0.15.0