final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model

AmirlyPhd/final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model

final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model is a machine learning model from AmirlyPhd. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

Downloads · 30 days

25% of all-time downloads

All-time downloads

Public

Parameters

110M

438 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors438 MB · 100%

At a glance

Model type: bert
Access: Public
Created: Nov 9, 2024
Updated: Nov 9, 2024
SHA: 8b8c7e1f

Model card

From the Hugging Face model README

final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.7809
Accuracy: 0.5419
F1: 0.5
Precision: 0.5
Recall: 0.5

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: 16
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.8374	0.06	50	1.8580	0.1350	0.0735	0.0910	0.1771
1.1067	0.12	100	1.7265	0.4003	0.2554	0.3101	0.3385
0.5553	0.18	150	0.9466	0.7571	0.5029	0.5522	0.5495
0.2362	0.25	200	0.8960	0.8595	0.7592	0.7701	0.7770
0.2549	0.31	250	0.9180	0.8623	0.7585	0.7737	0.7785
0.1716	0.37	300	0.9975	0.8646	0.7807	0.7630	0.8138
0.2299	0.43	350	0.8119	0.8614	0.7843	0.7481	0.8354
0.1657	0.49	400	0.9501	0.8657	0.7878	0.7724	0.8215
0.1585	0.55	450	1.0274	0.8661	0.7962	0.7708	0.8367
0.1856	0.61	500	1.0357	0.8675	0.7948	0.7554	0.8510
0.1002	0.67	550	1.1383	0.8657	0.7978	0.7673	0.8423
0.1505	0.74	600	1.0459	0.8678	0.7981	0.7646	0.8477
0.1264	0.8	650	0.9859	0.8692	0.8048	0.7781	0.8467
0.13	0.86	700	1.0246	0.8678	0.7947	0.7569	0.8496
0.1151	0.92	750	0.5834	0.8764	0.8223	0.8837	0.8621
0.1776	0.98	800	1.0297	0.8675	0.7903	0.7773	0.8202
0.0488	1.04	850	1.0348	0.8700	0.8038	0.7724	0.8504
0.0752	1.1	900	1.1004	0.8675	0.7839	0.7725	0.8136
0.0696	1.17	950	1.1802	0.8701	0.8020	0.7756	0.8438
0.0743	1.23	1000	1.1167	0.8695	0.8065	0.7791	0.8490
0.0757	1.29	1050	1.1188	0.8704	0.8078	0.7795	0.8516
0.0757	1.35	1100	0.7870	0.8692	0.8062	0.7816	0.8460
0.0847	1.41	1150	1.0518	0.8698	0.8045	0.7791	0.8450
0.0502	1.47	1200	1.1333	0.8689	0.7993	0.7679	0.8469
0.0516	1.53	1250	1.2185	0.8626	0.7707	0.7217	0.8429
0.0841	1.6	1300	1.2722	0.8689	0.8026	0.7798	0.8404
0.1063	1.66	1350	1.2437	0.8690	0.8008	0.7808	0.8362
0.097	1.72	1400	1.1243	0.8684	0.7930	0.7836	0.8201
0.0746	1.78	1450	1.2221	0.8701	0.8072	0.7801	0.8498
0.0726	1.84	1500	0.7919	0.8676	0.8076	0.7799	0.8500
0.0779	1.9	1550	1.1613	0.8704	0.8092	0.7837	0.8500
0.0895	1.96	1600	1.0377	0.8704	0.8060	0.7788	0.8485
0.0481	2.02	1650	1.1583	0.8710	0.8087	0.7810	0.8520
0.0266	2.09	1700	1.1655	0.8687	0.8020	0.7734	0.8452
0.0403	2.15	1750	1.2421	0.8707	0.8066	0.7777	0.8509
0.0116	2.21	1800	1.2306	0.8701	0.8048	0.7778	0.8474
0.0287	2.27	1850	1.2461	0.8700	0.8057	0.7784	0.8487
0.0197	2.33	1900	1.2199	0.8612	0.7937	0.7568	0.8451
0.0325	2.39	1950	1.3021	0.8703	0.8051	0.7785	0.8472
0.0443	2.45	2000	1.2395	0.8703	0.8061	0.7771	0.8503
0.0189	2.52	2050	1.2496	0.8704	0.8052	0.7812	0.8449
0.0056	2.58	2100	1.2561	0.8706	0.8073	0.7772	0.8527
0.0188	2.64	2150	1.2711	0.8706	0.8053	0.7818	0.8443
0.0287	2.7	2200	1.2728	0.8706	0.8068	0.7781	0.8504
0.0487	2.76	2250	1.1602	0.8710	0.8074	0.7802	0.8499
0.0409	2.82	2300	1.0628	0.8706	0.8061	0.7760	0.8510
0.053	2.88	2350	1.1891	0.8707	0.8076	0.7779	0.8526
0.0109	2.94	2400	1.2429	0.8700	0.8065	0.7811	0.8476
0.0392	3.01	2450	1.2635	0.8709	0.8058	0.7759	0.8509
0.0237	3.07	2500	1.2678	0.8703	0.8023	0.7817	0.8386
0.007	3.13	2550	1.2495	0.8709	0.8077	0.7812	0.8497
0.009	3.19	2600	1.2368	0.8715	0.8091	0.7807	0.8529
0.0022	3.25	2650	1.2436	0.8707	0.8055	0.7829	0.8435
0.0175	3.31	2700	1.2469	0.8712	0.8079	0.7818	0.8493
0.0037	3.37	2750	1.2342	0.8710	0.8068	0.7781	0.8510
0.0091	3.44	2800	1.2489	0.8701	0.8041	0.7780	0.8459
0.0008	3.5	2850	1.2252	0.8709	0.8059	0.7742	0.8532
0.005	3.56	2900	1.2281	0.8696	0.8030	0.7693	0.8522
0.0004	3.62	2950	1.2746	0.8704	0.8052	0.7760	0.8501
0.0009	3.68	3000	1.2903	0.8706	0.8054	0.7760	0.8504
0.001	3.74	3050	1.2960	0.8712	0.8060	0.7780	0.8492
0.0002	3.8	3100	1.3036	0.8712	0.8060	0.7780	0.8492
0.0185	3.87	3150	1.3224	0.8701	0.8048	0.7797	0.8453
0.0013	3.93	3200	1.3236	0.8707	0.8064	0.7780	0.8503
0.0003	3.99	3250	1.3241	0.8710	0.8068	0.7780	0.8509
0.0128	4.05	3300	1.3175	0.8704	0.8075	0.7794	0.8504
0.0005	4.11	3350	1.3160	0.8709	0.8078	0.7797	0.8508
0.0147	4.17	3400	1.3180	0.8712	0.8078	0.7803	0.8505
0.0064	4.23	3450	1.3197	0.8710	0.8076	0.7793	0.8510
0.0009	4.29	3500	1.3245	0.8712	0.8080	0.7800	0.8512
0.0002	4.36	3550	1.3336	0.8712	0.8079	0.7820	0.8491
0.0131	4.42	3600	1.3113	0.8710	0.8076	0.7794	0.8511
0.0003	4.48	3650	1.3200	0.8712	0.8079	0.7820	0.8491
0.0005	4.54	3700	1.3258	0.8712	0.8080	0.7841	0.8472
0.0102	4.6	3750	1.3177	0.8712	0.8079	0.7797	0.8512
0.0161	4.66	3800	1.3042	0.8712	0.8077	0.7794	0.8512
0.0178	4.72	3850	1.3133	0.8710	0.8076	0.7794	0.8511
0.0067	4.79	3900	1.3154	0.8709	0.8076	0.7793	0.8510
0.0191	4.85	3950	1.3187	0.8715	0.8103	0.7843	0.8516
0.0048	4.91	4000	1.3218	0.8713	0.8091	0.7842	0.8492
0.0046	4.97	4050	1.3220	0.8715	0.8103	0.7843	0.8516

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Type: bert
Created: Nov 9, 2024
Updated: Nov 9, 2024