Merged-Server-praj

Anwaarma/Merged-Server-praj

Merged-Server-praj is a text classification model from Anwaarma. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 24, 2023
Updated: Dec 24, 2023
SHA: 684f203c

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Base models

Anwaarma/BP-server02andinteger03

Model card

From the Hugging Face model README

Merged-Server-praj

This model is a fine-tuned version of Anwaarma/BP-server02andinteger03 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6453
Accuracy: 0.619
F1: 0.7647

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.0	50	0.6887	0.518	0.4747
No log	0.0	100	0.6872	0.539	0.4913
No log	0.01	150	0.6870	0.556	0.5124
No log	0.01	200	0.6845	0.554	0.5490
No log	0.01	250	0.6823	0.555	0.5509
No log	0.01	300	0.6818	0.545	0.5339
No log	0.02	350	0.6841	0.561	0.5207
No log	0.02	400	0.6845	0.566	0.5402
No log	0.02	450	0.6792	0.566	0.5650
0.6886	0.02	500	0.6808	0.532	0.4867
0.6886	0.02	550	0.6821	0.527	0.4643
0.6886	0.03	600	0.6777	0.577	0.5729
0.6886	0.03	650	0.6758	0.58	0.58
0.6886	0.03	700	0.6762	0.585	0.5793
0.6886	0.03	750	0.6743	0.577	0.5731
0.6886	0.04	800	0.6731	0.577	0.5762
0.6886	0.04	850	0.6762	0.582	0.5534
0.6886	0.04	900	0.6749	0.568	0.5458
0.6886	0.04	950	0.6739	0.578	0.5746
0.6819	0.04	1000	0.6715	0.588	0.588
0.6819	0.05	1050	0.6741	0.579	0.5524
0.6819	0.05	1100	0.6712	0.592	0.5894
0.6819	0.05	1150	0.6692	0.594	0.5905
0.6819	0.05	1200	0.6683	0.597	0.5957
0.6819	0.06	1250	0.6757	0.566	0.5209
0.6819	0.06	1300	0.6676	0.6	0.5882
0.6819	0.06	1350	0.6655	0.598	0.5943
0.6819	0.06	1400	0.6671	0.584	0.5724
0.6819	0.06	1450	0.6631	0.587	0.5812
0.6763	0.07	1500	0.6614	0.62	0.6140
0.6763	0.07	1550	0.6610	0.603	0.6018
0.6763	0.07	1600	0.6615	0.589	0.5843
0.6763	0.07	1650	0.6597	0.633	0.6326
0.6763	0.08	1700	0.6561	0.607	0.6025
0.6763	0.08	1750	0.6515	0.632	0.6318
0.6763	0.08	1800	0.6509	0.635	0.6323
0.6763	0.08	1850	0.6538	0.604	0.5903
0.6763	0.08	1900	0.6483	0.632	0.632
0.6763	0.09	1950	0.6513	0.616	0.6142
0.6659	0.09	2000	0.6475	0.618	0.6166
0.6659	0.09	2050	0.6443	0.613	0.6106
0.6659	0.09	2100	0.6482	0.629	0.6261
0.6659	0.1	2150	0.6552	0.604	0.5837
0.6659	0.1	2200	0.6492	0.618	0.6042
0.6659	0.1	2250	0.6441	0.63	0.6256
0.6659	0.1	2300	0.6447	0.63	0.6293
0.6659	0.1	2350	0.6429	0.633	0.6317
0.6659	0.11	2400	0.6450	0.62	0.6200
0.6659	0.11	2450	0.6480	0.639	0.6358
0.6577	0.11	2500	0.6480	0.601	0.5844
0.6577	0.11	2550	0.6422	0.641	0.6402
0.6577	0.11	2600	0.6407	0.623	0.6177
0.6577	0.12	2650	0.6393	0.634	0.6336
0.6577	0.12	2700	0.6405	0.635	0.6320
0.6577	0.12	2750	0.6422	0.642	0.6413
0.6577	0.12	2800	0.6355	0.632	0.6271
0.6577	0.13	2850	0.6351	0.622	0.6150
0.6577	0.13	2900	0.6331	0.658	0.6569
0.6577	0.13	2950	0.6352	0.656	0.6548
0.6569	0.13	3000	0.6385	0.609	0.5927
0.6569	0.13	3050	0.6307	0.645	0.6449
0.6569	0.14	3100	0.6304	0.639	0.6324
0.6569	0.14	3150	0.6268	0.654	0.6540
0.6569	0.14	3200	0.6297	0.641	0.6402
0.6569	0.14	3250	0.6293	0.651	0.6488
0.6569	0.15	3300	0.6251	0.66	0.66
0.6569	0.15	3350	0.6207	0.653	0.6513
0.6569	0.15	3400	0.6184	0.649	0.6470
0.6569	0.15	3450	0.6176	0.653	0.6523
0.6394	0.15	3500	0.6195	0.654	0.6527
0.6394	0.16	3550	0.6172	0.655	0.6535
0.6394	0.16	3600	0.6168	0.659	0.6590
0.6394	0.16	3650	0.6138	0.664	0.6631
0.6394	0.16	3700	0.6228	0.659	0.6478
0.6394	0.17	3750	0.6117	0.662	0.6608
0.6394	0.17	3800	0.6121	0.645	0.6443
0.6394	0.17	3850	0.6099	0.652	0.6515
0.6394	0.17	3900	0.6128	0.657	0.6515
0.6394	0.17	3950	0.6103	0.65	0.6499
0.6366	0.18	4000	0.6149	0.652	0.6447
0.6366	0.18	4050	0.6111	0.652	0.6518
0.6366	0.18	4100	0.6098	0.651	0.6502
0.6366	0.18	4150	0.6072	0.666	0.6640
0.6366	0.19	4200	0.6065	0.669	0.6646
0.6366	0.19	4250	0.6057	0.658	0.6531
0.6366	0.19	4300	0.6044	0.667	0.6634
0.6366	0.19	4350	0.6061	0.656	0.6451
0.6366	0.19	4400	0.5962	0.666	0.6660
0.6366	0.2	4450	0.5936	0.664	0.6637
0.6275	0.2	4500	0.5978	0.651	0.6508
0.6275	0.2	4550	0.5998	0.664	0.6601
0.6275	0.2	4600	0.5967	0.657	0.6560
0.6275	0.21	4650	0.5931	0.671	0.6708
0.6275	0.21	4700	0.5953	0.686	0.6856
0.6275	0.21	4750	0.6119	0.643	0.6331
0.6275	0.21	4800	0.6035	0.657	0.6497
0.6275	0.21	4850	0.5864	0.676	0.6756
0.6275	0.22	4900	0.5876	0.681	0.6809
0.6275	0.22	4950	0.5864	0.677	0.6762
0.6279	0.22	5000	0.5876	0.677	0.6770
0.6279	0.22	5050	0.5876	0.666	0.6636
0.6279	0.23	5100	0.5848	0.675	0.6745
0.6279	0.23	5150	0.5904	0.672	0.6719
0.6279	0.23	5200	0.5854	0.672	0.6718
0.6279	0.23	5250	0.5864	0.673	0.6709

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: bert
Created: Dec 24, 2023
Updated: Dec 24, 2023