Merged-MM-praj

Anwaarma/Merged-MM-praj

Merged-MM-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. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Dec 24, 2023
Updated: Dec 24, 2023
SHA: 94551f20

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Base models

prajjwal1/bert-tiny

Model card

From the Hugging Face model README

Merged-MM-praj

This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5525
Accuracy: 0.7777
F1: 0.8749

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: 3e-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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.0	50	0.6929	0.526	0.3813
No log	0.0	100	0.6938	0.48	0.3125
No log	0.01	150	0.6971	0.479	0.3103
No log	0.01	200	0.6948	0.479	0.3103
No log	0.01	250	0.6938	0.479	0.3103
No log	0.01	300	0.6939	0.479	0.3103
No log	0.01	350	0.6927	0.521	0.3587
No log	0.02	400	0.6931	0.501	0.4988
No log	0.02	450	0.6944	0.479	0.3103
0.6942	0.02	500	0.6954	0.479	0.3103
0.6942	0.02	550	0.6960	0.479	0.3103
0.6942	0.02	600	0.6934	0.486	0.3322
0.6942	0.02	650	0.6970	0.479	0.3103
0.6942	0.03	700	0.6929	0.535	0.4767
0.6942	0.03	750	0.6931	0.499	0.4609
0.6942	0.03	800	0.6952	0.479	0.3103
0.6942	0.03	850	0.6933	0.48	0.3160
0.6942	0.03	900	0.6979	0.479	0.3103
0.6942	0.04	950	0.6940	0.479	0.3103
0.6938	0.04	1000	0.6915	0.521	0.3569
0.6938	0.04	1050	0.6942	0.479	0.3103
0.6938	0.04	1100	0.6884	0.519	0.3630
0.6938	0.04	1150	0.6849	0.596	0.5817
0.6938	0.05	1200	0.6849	0.547	0.5131
0.6938	0.05	1250	0.6771	0.568	0.5502
0.6938	0.05	1300	0.6792	0.572	0.5558
0.6938	0.05	1350	0.6889	0.55	0.5161
0.6938	0.05	1400	0.6792	0.59	0.5828
0.6938	0.06	1450	0.6729	0.602	0.5987
0.6781	0.06	1500	0.6702	0.592	0.5822
0.6781	0.06	1550	0.6711	0.578	0.5633
0.6781	0.06	1600	0.6642	0.607	0.6024
0.6781	0.06	1650	0.6624	0.592	0.5819
0.6781	0.07	1700	0.6585	0.595	0.5883
0.6781	0.07	1750	0.6543	0.584	0.5740
0.6781	0.07	1800	0.6452	0.6	0.5926
0.6781	0.07	1850	0.6355	0.615	0.6106
0.6781	0.07	1900	0.6280	0.615	0.6090
0.6781	0.07	1950	0.6209	0.621	0.6139
0.6465	0.08	2000	0.6178	0.632	0.6247
0.6465	0.08	2050	0.6133	0.641	0.6303
0.6465	0.08	2100	0.6132	0.629	0.6218
0.6465	0.08	2150	0.6155	0.63	0.6289
0.6465	0.08	2200	0.5984	0.635	0.6322
0.6465	0.09	2250	0.6065	0.633	0.6102
0.6465	0.09	2300	0.5968	0.629	0.6063
0.6465	0.09	2350	0.5871	0.649	0.6411
0.6465	0.09	2400	0.5824	0.64	0.6218
0.6465	0.09	2450	0.5812	0.643	0.6390
0.6042	0.1	2500	0.5790	0.644	0.6355
0.6042	0.1	2550	0.5744	0.654	0.6507
0.6042	0.1	2600	0.5679	0.641	0.6292
0.6042	0.1	2650	0.5707	0.644	0.6311
0.6042	0.1	2700	0.5707	0.652	0.6439
0.6042	0.11	2750	0.5680	0.661	0.6569
0.6042	0.11	2800	0.5592	0.67	0.6684
0.6042	0.11	2850	0.5557	0.678	0.6758
0.6042	0.11	2900	0.5579	0.671	0.6690
0.6042	0.11	2950	0.5490	0.692	0.6909
0.5834	0.11	3000	0.5474	0.688	0.6858
0.5834	0.12	3050	0.5447	0.696	0.6902
0.5834	0.12	3100	0.5456	0.699	0.6985
0.5834	0.12	3150	0.5592	0.675	0.6628
0.5834	0.12	3200	0.5442	0.69	0.6856
0.5834	0.12	3250	0.5424	0.698	0.6974
0.5834	0.13	3300	0.5464	0.691	0.6907
0.5834	0.13	3350	0.5433	0.693	0.6922
0.5834	0.13	3400	0.5400	0.746	0.7461
0.5834	0.13	3450	0.5406	0.712	0.7091
0.5551	0.13	3500	0.5367	0.738	0.7376
0.5551	0.14	3550	0.5354	0.713	0.7091
0.5551	0.14	3600	0.5377	0.74	0.7400
0.5551	0.14	3650	0.5342	0.751	0.7506
0.5551	0.14	3700	0.5386	0.701	0.6992
0.5551	0.14	3750	0.5395	0.737	0.7368
0.5551	0.15	3800	0.5333	0.733	0.7330
0.5551	0.15	3850	0.5245	0.737	0.7371
0.5551	0.15	3900	0.5236	0.745	0.7451
0.5551	0.15	3950	0.5149	0.741	0.7400
0.5508	0.15	4000	0.5208	0.743	0.7422
0.5508	0.16	4050	0.5109	0.744	0.7440
0.5508	0.16	4100	0.5179	0.742	0.7398
0.5508	0.16	4150	0.5133	0.75	0.7499
0.5508	0.16	4200	0.5110	0.744	0.7416
0.5508	0.16	4250	0.5133	0.749	0.7476
0.5508	0.16	4300	0.5075	0.743	0.7410
0.5508	0.17	4350	0.5108	0.755	0.7544
0.5508	0.17	4400	0.5051	0.747	0.7465
0.5508	0.17	4450	0.5064	0.746	0.7455
0.5362	0.17	4500	0.5030	0.744	0.7441
0.5362	0.17	4550	0.5043	0.748	0.7476
0.5362	0.18	4600	0.5010	0.753	0.7531
0.5362	0.18	4650	0.4988	0.762	0.7616
0.5362	0.18	4700	0.4999	0.755	0.7548
0.5362	0.18	4750	0.5159	0.754	0.7529
0.5362	0.18	4800	0.4924	0.764	0.7639
0.5362	0.19	4850	0.4935	0.755	0.7549
0.5362	0.19	4900	0.4874	0.76	0.7601
0.5362	0.19	4950	0.4859	0.759	0.7591
0.5226	0.19	5000	0.4901	0.761	0.7610
0.5226	0.19	5050	0.4740	0.779	0.7790
0.5226	0.2	5100	0.4799	0.783	0.7831
0.5226	0.2	5150	0.4833	0.771	0.7698
0.5226	0.2	5200	0.4879	0.759	0.7561
0.5226	0.2	5250	0.4812	0.772	0.7719
0.5226	0.2	5300	0.4825	0.772	0.7715
0.5226	0.2	5350	0.4791	0.775	0.7744
0.5226	0.21	5400	0.4749	0.773	0.7729
0.5226	0.21	5450	0.4691	0.782	0.7811
0.5055	0.21	5500	0.4752	0.78	0.7791
0.5055	0.21	5550	0.4621	0.766	0.7645
0.5055	0.21	5600	0.4628	0.779	0.7790
0.5055	0.22	5650	0.4543	0.776	0.7760
0.5055	0.22	5700	0.4548	0.786	0.7861
0.5055	0.22	5750	0.4578	0.777	0.7763
0.5055	0.22	5800	0.4684	0.778	0.7780
0.5055	0.22	5850	0.4626	0.775	0.7751
0.5055	0.23	5900	0.4714	0.785	0.7850
0.5055	0.23	5950	0.4514	0.79	0.7896
0.4985	0.23	6000	0.4541	0.773	0.7731
0.4985	0.23	6050	0.4587	0.788	0.7876
0.4985	0.23	6100	0.4523	0.787	0.7867
0.4985	0.24	6150	0.4441	0.787	0.7870
0.4985	0.24	6200	0.4529	0.784	0.7841
0.4985	0.24	6250	0.4512	0.784	0.7840
0.4985	0.24	6300	0.4545	0.777	0.7757
0.4985	0.24	6350	0.4399	0.788	0.7874
0.4985	0.25	6400	0.4478	0.794	0.7939
0.4985	0.25	6450	0.4495	0.793	0.7930
0.4937	0.25	6500	0.4454	0.792	0.7913
0.4937	0.25	6550	0.4438	0.795	0.7950
0.4937	0.25	6600	0.4476	0.795	0.7948
0.4937	0.25	6650	0.4448	0.794	0.7939
0.4937	0.26	6700	0.4472	0.791	0.7911
0.4937	0.26	6750	0.4431	0.793	0.7924
0.4937	0.26	6800	0.4434	0.796	0.7958
0.4937	0.26	6850	0.4340	0.802	0.802
0.4937	0.26	6900	0.4502	0.786	0.7848
0.4937	0.27	6950	0.4349	0.797	0.7964
0.4826	0.27	7000	0.4348	0.79	0.7894
0.4826	0.27	7050	0.4321	0.788	0.7875
0.4826	0.27	7100	0.4300	0.787	0.7868
0.4826	0.27	7150	0.4346	0.78	0.7779
0.4826	0.28	7200	0.4246	0.802	0.8020
0.4826	0.28	7250	0.4273	0.793	0.7930
0.4826	0.28	7300	0.4346	0.79	0.7894
0.4826	0.28	7350	0.4358	0.789	0.7887
0.4826	0.28	7400	0.4368	0.788	0.7871
0.4826	0.29	7450	0.4426	0.784	0.7841
0.4756	0.29	7500	0.4312	0.802	0.8019
0.4756	0.29	7550	0.4303	0.795	0.7944
0.4756	0.29	7600	0.4391	0.792	0.7916
0.4756	0.29	7650	0.4325	0.793	0.7922
0.4756	0.29	7700	0.4283	0.793	0.7920
0.4756	0.3	7750	0.4271	0.799	0.7991

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
License: mit
Created: Dec 24, 2023
Updated: Dec 24, 2023