metadata

tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: >-
      final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model
    results: []

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