histv4_ftis_noPretrain

Arthur-Tsai/histv4_ftis_noPretrain

histv4_ftis_noPretrain is a machine learning model from Arthur-Tsai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

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

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At a glance

Library: transformers
Model type: hierarchical-sentence-transformer-v2
Access: Public
Created: Feb 3, 2025
Updated: Mar 16, 2025
SHA: 2d295235

Model card

From the Hugging Face model README

histv4_ftis_noPretrain

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

Loss: 22.9027
Accuracy: 0.9708
Macro F1: 0.9387

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: 0.0001
train_batch_size: 8
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 6733
training_steps: 134675

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
5.6706	0.0050	673	163.1458	0.5351	0.1372
3.2025	1.0050	1346	66.2205	0.6024	0.1805
2.2709	2.0050	2019	23.3143	0.6566	0.2508
1.8759	3.0050	2692	14.8860	0.6862	0.3239
1.7911	4.0050	3365	9.8597	0.7467	0.4130
1.4354	5.0050	4038	6.3745	0.7854	0.4905
1.2613	6.0050	4711	7.3708	0.8181	0.5386
1.0953	7.0049	5384	9.4678	0.8289	0.5704
0.9656	8.0049	6057	10.9515	0.8501	0.6187
0.7637	9.0049	6730	9.6294	0.8727	0.6558
0.743	10.0049	7403	12.9812	0.8988	0.7010
0.5359	11.0049	8076	19.9032	0.9059	0.7257
0.5607	12.0049	8749	19.8261	0.9161	0.7501
0.4005	13.0049	9422	16.8337	0.9188	0.7727
0.3745	14.0049	10095	14.7905	0.9308	0.7819
0.3704	15.0049	10768	15.6947	0.9362	0.8003
0.3194	16.0049	11441	12.9565	0.9404	0.8072
0.2521	17.0049	12114	13.3948	0.9446	0.8214
0.2788	18.0049	12787	12.1413	0.9448	0.8331
0.24	19.0049	13460	9.2497	0.9469	0.8309
0.217	20.0048	14133	10.6283	0.9502	0.8490
0.2065	21.0048	14806	13.5652	0.9512	0.8493
0.1739	22.0048	15479	7.6495	0.9566	0.8716
0.1849	23.0048	16152	10.3599	0.9560	0.8739
0.1554	24.0048	16825	8.4639	0.9541	0.8713
0.1688	25.0048	17498	8.1545	0.9565	0.8774
0.1078	26.0048	18171	6.9724	0.9590	0.8811
0.1337	27.0048	18844	6.4333	0.9567	0.8795
0.1005	28.0048	19517	7.5471	0.9566	0.8816
0.0902	29.0048	20190	6.9489	0.9608	0.8921
0.1022	30.0048	20863	7.2287	0.9597	0.8856
0.0774	31.0048	21536	7.9467	0.9644	0.9068
0.0661	32.0048	22209	7.0721	0.9611	0.8957
0.0754	33.0048	22882	6.3389	0.9631	0.8987
0.0849	34.0047	23555	7.7401	0.9648	0.9064
0.0683	35.0047	24228	7.2394	0.9647	0.9113
0.0551	36.0047	24901	6.8625	0.9660	0.9117
0.0558	37.0047	25574	6.9998	0.9657	0.9168
0.0542	38.0047	26247	9.0917	0.9667	0.9159
0.0605	39.0047	26920	9.8845	0.9654	0.9136
0.0567	40.0047	27593	9.5249	0.9646	0.9159
0.0553	41.0047	28266	7.5163	0.9656	0.9174
0.0483	42.0047	28939	7.9149	0.9667	0.9198
0.0444	43.0047	29612	7.2773	0.9650	0.9156
0.0493	44.0047	30285	8.2501	0.9689	0.9009
0.042	45.0047	30958	8.3921	0.9668	0.9168
0.0332	46.0047	31631	7.8757	0.9668	0.9179
0.0562	47.0046	32304	10.3526	0.9671	0.9195
0.0463	48.0046	32977	10.4190	0.9683	0.9207
0.0337	49.0046	33650	8.7521	0.9680	0.9160
0.0299	50.0046	34323	8.7141	0.9666	0.9248
0.038	51.0046	34996	8.1480	0.9670	0.9174
0.0344	52.0046	35669	9.5917	0.9671	0.9102
0.0378	53.0046	36342	9.7323	0.9688	0.9243
0.0316	54.0046	37015	10.4115	0.9639	0.9186
0.0314	55.0046	37688	10.1031	0.9676	0.9219
0.0266	56.0046	38361	11.5819	0.9692	0.9295
0.0281	57.0046	39034	9.8724	0.9666	0.9185
0.0286	58.0046	39707	9.4405	0.9683	0.9241
0.0336	59.0046	40380	11.8320	0.9694	0.9283
0.0251	60.0046	41053	12.5118	0.9688	0.9333
0.0217	61.0045	41726	15.1022	0.9705	0.9322
0.0235	62.0045	42399	11.6105	0.9680	0.9263
0.0229	63.0045	43072	12.1260	0.9672	0.9257
0.0222	64.0045	43745	12.9063	0.9658	0.9323
0.0242	65.0045	44418	9.6198	0.9680	0.9285
0.0218	66.0045	45091	13.3342	0.9689	0.9320
0.0206	67.0045	45764	15.4714	0.9695	0.9344
0.0189	68.0045	46437	10.6895	0.9676	0.9269
0.0196	69.0045	47110	13.4871	0.9693	0.9312
0.017	70.0045	47783	14.8361	0.9686	0.9295
0.0185	71.0045	48456	13.1012	0.9677	0.9301
0.0177	72.0045	49129	12.6586	0.9673	0.9257
0.0188	73.0045	49802	16.0946	0.9689	0.9293
0.0164	74.0044	50475	14.0022	0.9700	0.9288
0.0162	75.0044	51148	10.8041	0.9672	0.9267
0.0188	76.0044	51821	12.3388	0.9675	0.9259
0.0158	77.0044	52494	14.4179	0.9697	0.9321
0.02	78.0044	53167	13.8769	0.9700	0.9329
0.0179	79.0044	53840	13.2398	0.9673	0.9306
0.0145	80.0044	54513	14.6983	0.9704	0.9281
0.0136	81.0044	55186	15.3679	0.9687	0.9362
0.0128	82.0044	55859	15.5807	0.9715	0.9356
0.0147	83.0044	56532	12.4938	0.9677	0.9299
0.017	84.0044	57205	14.0998	0.9707	0.9362
0.0122	85.0044	57878	15.6111	0.9685	0.9345
0.0151	86.0044	58551	11.5770	0.9682	0.9294
0.0118	87.0044	59224	13.1092	0.9696	0.9320
0.0136	88.0043	59897	14.3885	0.9681	0.9281
0.0126	89.0043	60570	15.1592	0.9697	0.9300
0.0147	90.0043	61243	16.0319	0.9690	0.9364
0.0127	91.0043	61916	14.8849	0.9694	0.9306
0.0123	92.0043	62589	15.1080	0.9700	0.9373
0.012	93.0043	63262	12.8495	0.9690	0.9342
0.0124	94.0043	63935	12.7915	0.9690	0.9313
0.0102	95.0043	64608	18.5121	0.9703	0.9330
0.0105	96.0043	65281	17.5668	0.9702	0.9338
0.0108	97.0043	65954	17.9356	0.9687	0.9318
0.0096	98.0043	66627	15.8806	0.9703	0.9344
0.0089	99.0043	67300	12.6527	0.9701	0.9347
0.0092	100.0043	67973	19.8478	0.9701	0.9329
0.0098	101.0042	68646	16.3105	0.9703	0.9374
0.0091	102.0042	69319	19.2020	0.9691	0.9310
0.0106	103.0042	69992	21.6559	0.9698	0.9293
0.0092	104.0042	70665	18.8253	0.9695	0.9291
0.0084	105.0042	71338	20.1503	0.9693	0.9308
0.0081	106.0042	72011	18.6289	0.9706	0.9351
0.0072	107.0042	72684	19.9631	0.9704	0.9334
0.01	108.0042	73357	23.2103	0.9687	0.9270
0.0078	109.0042	74030	21.5416	0.9697	0.9358
0.0077	110.0042	74703	23.7068	0.9707	0.9341
0.0073	111.0042	75376	22.0645	0.9707	0.9372
0.0074	112.0042	76049	21.4179	0.9704	0.9299
0.0092	113.0042	76722	23.4124	0.9706	0.9365
0.0087	114.0042	77395	26.8832	0.9712	0.9315
0.0077	115.0041	78068	24.6745	0.9708	0.9382
0.0069	116.0041	78741	23.7589	0.9708	0.9387
0.0085	117.0041	79414	19.9101	0.9697	0.9306
0.0065	118.0041	80087	19.5109	0.9703	0.9359
0.0061	119.0041	80760	22.7762	0.9699	0.9290
0.0064	120.0041	81433	24.8343	0.9701	0.9324
0.0063	121.0041	82106	26.0722	0.9711	0.9360
0.0067	122.0041	82779	21.4954	0.9709	0.9317
0.0071	123.0041	83452	27.8143	0.9701	0.9324
0.0058	124.0041	84125	30.4180	0.9693	0.9324
0.0073	125.0041	84798	28.6532	0.9695	0.9315
0.0055	126.0041	85471	31.8040	0.9706	0.9319
0.0056	127.0041	86144	32.3538	0.9691	0.9293
0.0055	128.0040	86817	30.3247	0.9700	0.9310
0.0066	129.0040	87490	36.1489	0.9695	0.9343
0.0049	130.0040	88163	34.2556	0.9686	0.9323
0.0049	131.0040	88836	33.8613	0.9698	0.9371
0.0061	132.0040	89509	33.1326	0.9701	0.9321
0.0048	133.0040	90182	34.4226	0.9702	0.9300
0.0052	134.0040	90855	28.6254	0.9692	0.9338
0.0046	135.0040	91528	33.8544	0.9693	0.9307
0.0041	136.0040	92201	37.8675	0.9704	0.9332

Framework versions

Transformers 4.46.0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.1

Library: transformers
Type: hierarchical-sentence-transformer-v2
Created: Feb 3, 2025
Updated: Mar 16, 2025