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Arthur-Tsai/
histv4_ftis_pretrain_tssp-smlm_0329

Name: histv4_ftis_pretrain_tssp-smlm_0329
Availability: InStock

transformers

tensorboard

safetensors

hierarchical-sentence-transformer-v2

generated_from_trainer

Arthur-Tsai/histv4_pretrain_tssp-smlm

finetune

endpoints_compatible

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histv4_ftis_pretrain_tssp-smlm_0329

This model is a fine-tuned version of Arthur-Tsai/histv4_pretrain_tssp-smlm on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7599
Accuracy: 0.9610
Macro F1: 0.9058

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: 6732
training_steps: 134650

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
11.3916	0.0025	336	8.0277	0.4182	0.1156
6.0125	1.0025	672	5.2700	0.5958	0.1641
4.7491	2.0025	1008	4.0342	0.6281	0.1748
3.7009	3.0025	1344	2.8428	0.6588	0.2100
2.9014	4.0025	1680	2.2087	0.7034	0.2419
2.3003	5.0025	2016	1.9890	0.7256	0.2790
2.0895	6.0025	2352	1.8024	0.7479	0.3347
1.8415	7.0024	2688	1.6778	0.7750	0.3759
1.7152	8.0024	3024	1.5370	0.7951	0.4240
1.5497	9.0024	3360	1.4729	0.8037	0.4553
1.5593	10.0024	3696	1.3168	0.8210	0.5071
1.3738	11.0024	4032	1.2857	0.8339	0.5285
1.1876	12.0024	4368	1.1716	0.8400	0.5568
1.1746	13.0024	4704	1.1193	0.8558	0.5784
1.1063	14.0024	5040	1.0744	0.8567	0.5968
1.0336	15.0024	5376	1.0388	0.8662	0.6259
0.9161	16.0024	5712	1.1186	0.8622	0.6175
0.8923	17.0024	6048	0.9439	0.8857	0.6524
0.8547	18.0024	6384	0.9416	0.8893	0.6759
0.8217	19.0024	6720	0.8847	0.8918	0.6874
0.777	20.0023	7056	0.9041	0.8950	0.6807
0.7134	21.0023	7392	0.8724	0.9032	0.7039
0.7108	22.0023	7728	0.8556	0.9046	0.7303
0.7189	23.0023	8064	0.8142	0.9060	0.7210
0.6324	24.0023	8400	0.7806	0.9187	0.7417
0.5949	25.0023	8736	0.7460	0.9238	0.7552
0.5285	26.0023	9072	0.7482	0.9219	0.7618
0.5477	27.0023	9408	0.7793	0.9268	0.7690
0.5164	28.0023	9744	0.7374	0.9291	0.7781
0.5476	29.0023	10080	0.7050	0.9315	0.7851
0.5009	30.0023	10416	0.7018	0.9326	0.7882
0.4908	31.0023	10752	0.7106	0.9344	0.7893
0.462	32.0023	11088	0.7520	0.9336	0.7728
0.4704	33.0023	11424	0.6871	0.9346	0.7913
0.4612	34.0022	11760	0.6826	0.9387	0.8070
0.4707	35.0022	12096	0.7139	0.9403	0.7983
0.414	36.0022	12432	0.7585	0.9409	0.8161
0.4068	37.0022	12768	0.6720	0.9427	0.8141
0.4481	38.0022	13104	0.7381	0.9403	0.8195
0.3791	39.0022	13440	0.7072	0.9415	0.8226
0.3824	40.0022	13776	0.6873	0.9467	0.8287
0.3624	41.0022	14112	0.7320	0.9439	0.8107
0.372	42.0022	14448	0.6873	0.9440	0.8333
0.3576	43.0022	14784	0.6846	0.9461	0.8338
0.3481	44.0022	15120	0.7048	0.9496	0.8377
0.3567	45.0022	15456	0.7339	0.9465	0.8435
0.3651	46.0022	15792	0.7016	0.9414	0.8398
0.3462	47.0021	16128	0.6781	0.9453	0.8254
0.3427	48.0021	16464	0.7162	0.9485	0.8491
0.3447	49.0021	16800	0.6853	0.9491	0.8474
0.3354	50.0021	17136	0.6949	0.9520	0.8491
0.3327	51.0021	17472	0.6741	0.9482	0.8461
0.3232	52.0021	17808	0.6951	0.9501	0.8520
0.3226	53.0021	18144	0.7612	0.9483	0.8510
0.335	54.0021	18480	0.7019	0.9519	0.8601
0.3211	55.0021	18816	0.7500	0.9499	0.8546
0.3124	56.0021	19152	0.7240	0.9523	0.8643
0.3093	57.0021	19488	0.6935	0.9520	0.8588
0.3138	58.0021	19824	0.7082	0.9479	0.8654
0.3058	59.0021	20160	0.7292	0.9521	0.8591
0.3047	60.0020	20496	0.7327	0.9541	0.8659
0.3055	61.0020	20832	0.7506	0.9511	0.8668
0.3057	62.0020	21168	0.7143	0.9551	0.8493
0.3029	63.0020	21504	0.6925	0.9518	0.8617
0.2922	64.0020	21840	0.7074	0.9536	0.8654
0.295	65.0020	22176	0.7020	0.9532	0.8706
0.2983	66.0020	22512	0.6974	0.9521	0.8713
0.3077	67.0020	22848	0.7091	0.9527	0.8680
0.2905	68.0020	23184	0.7175	0.9540	0.8747
0.2805	69.0020	23520	0.7342	0.9523	0.8737
0.2837	70.0020	23856	0.7966	0.9533	0.8517
0.2874	71.0020	24192	0.6759	0.9556	0.8596
0.296	72.0020	24528	0.6929	0.9527	0.8558
0.2879	73.0020	24864	0.7687	0.9525	0.8740
0.2921	74.0019	25200	0.7167	0.9552	0.8817
0.2835	75.0019	25536	0.6643	0.9552	0.8625
0.2813	76.0019	25872	0.7766	0.9546	0.8592
0.2855	77.0019	26208	0.7541	0.9571	0.8624
0.2746	78.0019	26544	0.7661	0.9555	0.8847
0.2856	79.0019	26880	0.7422	0.9564	0.8833
0.2703	80.0019	27216	0.7156	0.9553	0.8862
0.2776	81.0019	27552	0.6946	0.9554	0.8670
0.27	82.0019	27888	0.7123	0.9566	0.8786
0.275	83.0019	28224	0.7023	0.9535	0.8836
0.269	84.0019	28560	0.7575	0.9556	0.8879
0.2704	85.0019	28896	0.7044	0.9569	0.8888
0.2663	86.0019	29232	0.8086	0.9527	0.8824
0.2707	87.0018	29568	0.7517	0.9564	0.8633
0.2622	88.0018	29904	0.7146	0.9564	0.8857
0.2692	89.0018	30240	0.7903	0.9572	0.8702
0.2635	90.0018	30576	0.7233	0.9564	0.8858
0.2644	91.0018	30912	0.6877	0.9581	0.8903
0.2671	92.0018	31248	0.7469	0.9570	0.8895
0.2574	93.0018	31584	0.7492	0.9548	0.8669
0.2587	94.0018	31920	0.6922	0.9586	0.8713
0.2652	95.0018	32256	0.8032	0.9582	0.8919
0.2626	96.0018	32592	0.7381	0.9558	0.8698
0.2622	97.0018	32928	0.7241	0.9550	0.8862
0.2541	98.0018	33264	0.6927	0.9563	0.8896
0.2579	99.0018	33600	0.7792	0.9584	0.8922
0.2547	100.0018	33936	0.7461	0.9592	0.8918
0.258	101.0017	34272	0.7134	0.9575	0.8749
0.2628	102.0017	34608	0.7115	0.9573	0.8941
0.2588	103.0017	34944	0.8139	0.9551	0.8923
0.2642	104.0017	35280	0.7333	0.9591	0.8942
0.2536	105.0017	35616	0.6827	0.9611	0.8727
0.2494	106.0017	35952	0.8377	0.9568	0.8926
0.2546	107.0017	36288	0.7951	0.9560	0.8927
0.2536	108.0017	36624	0.6921	0.9571	0.8748
0.2494	109.0017	36960	0.7702	0.9591	0.8930
0.2495	110.0017	37296	0.7993	0.9604	0.8771
0.2456	111.0017	37632	0.7948	0.9574	0.8733
0.2566	112.0017	37968	0.7221	0.9603	0.8976
0.2502	113.0017	38304	0.7206	0.9573	0.8689
0.2504	114.0016	38640	0.8227	0.9573	0.8968
0.2542	115.0016	38976	0.7220	0.9557	0.8720
0.2499	116.0016	39312	0.7095	0.9572	0.8756
0.2448	117.0016	39648	0.7044	0.9591	0.8936
0.2463	118.0016	39984	0.7497	0.9578	0.8770
0.2439	119.0016	40320	0.7419	0.9579	0.8951
0.2444	120.0016	40656	0.7349	0.9597	0.9009
0.2423	121.0016	40992	0.6960	0.9591	0.8816
0.2443	122.0016	41328	0.8190	0.9590	0.8935
0.2483	123.0016	41664	0.7469	0.9581	0.8959
0.2509	124.0016	42000	0.6861	0.9598	0.9000
0.2447	125.0016	42336	0.8074	0.9567	0.8811
0.2457	126.0016	42672	0.7880	0.9556	0.8759
0.245	127.0016	43008	0.6982	0.9594	0.8958
0.2459	128.0015	43344	0.7395	0.9580	0.8965
0.2404	129.0015	43680	0.8232	0.9601	0.8959
0.2441	130.0015	44016	0.7924	0.9587	0.8979
0.2385	131.0015	44352	0.7226	0.9619	0.8999
0.2428	132.0015	44688	0.7610	0.9554	0.8997
0.2415	133.0015	45024	0.7271	0.9573	0.8989
0.2384	134.0015	45360	0.7247	0.9590	0.9012
0.2401	135.0015	45696	0.7528	0.9566	0.8738
0.2376	136.0015	46032	0.7993	0.9546	0.8944
0.2399	137.0015	46368	0.6989	0.9579	0.8974
0.2359	138.0015	46704	0.7358	0.9586	0.8973
0.2416	139.0015	47040	0.7672	0.9535	0.8961
0.2394	140.0015	47376	0.8060	0.9580	0.8959
0.2393	141.0014	47712	0.8144	0.9575	0.8815
0.2348	142.0014	48048	0.7318	0.9578	0.8808
0.2387	143.0014	48384	0.7526	0.9598	0.8820
0.2411	144.0014	48720	0.7875	0.9579	0.8975
0.2354	145.0014	49056	0.8195	0.9596	0.8986
0.2375	146.0014	49392	0.8084	0.9568	0.8810
0.241	147.0014	49728	0.8042	0.9567	0.8821
0.2397	148.0014	50064	0.7167	0.9590	0.9023
0.2339	149.0014	50400	0.8129	0.9592	0.8864
0.2363	150.0014	50736	0.7552	0.9583	0.8782
0.2349	151.0014	51072	0.7979	0.9564	0.8792
0.2365	152.0014	51408	0.7781	0.9603	0.8811
0.2354	153.0014	51744	0.7425	0.9582	0.8784
0.2383	154.0014	52080	0.7508	0.9600	0.8983
0.2337	155.0013	52416	0.7715	0.9588	0.8813
0.2392	156.0013	52752	0.7476	0.9594	0.9030
0.2323	157.0013	53088	0.8146	0.9541	0.9010
0.2323	158.0013	53424	0.7687	0.9605	0.8828
0.2321	159.0013	53760	0.7765	0.9564	0.8793
0.2279	160.0013	54096	0.6969	0.9581	0.8969
0.2318	161.0013	54432	0.8148	0.9583	0.8784
0.2337	162.0013	54768	0.7911	0.9560	0.9038
0.2328	163.0013	55104	0.7445	0.9586	0.9003
0.2327	164.0013	55440	0.7848	0.9596	0.8844
0.2341	165.0013	55776	0.8535	0.9590	0.8811
0.2341	166.0013	56112	0.7657	0.9573	0.8992
0.232	167.0013	56448	0.7364	0.9587	0.8803
0.2354	168.0012	56784	0.7325	0.9609	0.9045
0.2362	169.0012	57120	0.7578	0.9575	0.9021
0.2292	170.0012	57456	0.8224	0.9590	0.8789
0.2315	171.0012	57792	0.7437	0.9586	0.8831
0.2284	172.0012	58128	0.7082	0.9611	0.8855
0.2315	173.0012	58464	0.7512	0.9605	0.8986
0.2273	174.0012	58800	0.8463	0.9580	0.8999
0.2259	175.0012	59136	0.7478	0.9608	0.8798
0.2272	176.0012	59472	0.6928	0.9619	0.9088
0.2301	177.0012	59808	0.7414	0.9586	0.9035
0.2283	178.0012	60144	0.6533	0.9603	0.8823
0.2269	179.0012	60480	0.7978	0.9593	0.8845
0.2296	180.0012	60816	0.8178	0.9589	0.8991
0.2241	181.0012	61152	0.7398	0.9589	0.8841
0.2276	182.0011	61488	0.7210	0.9627	0.8855
0.2243	183.0011	61824	0.8306	0.9551	0.8780
0.2252	184.0011	62160	0.8134	0.9612	0.9048
0.2279	185.0011	62496	0.7744	0.9609	0.9071
0.2276	186.0011	62832	0.7818	0.9606	0.9054
0.2269	187.0011	63168	0.8698	0.9601	0.9051
0.2255	188.0011	63504	0.8384	0.9583	0.9028
0.2245	189.0011	63840	0.8141	0.9588	0.9056
0.2247	190.0011	64176	0.7958	0.9603	0.9050
0.2227	191.0011	64512	0.7830	0.9594	0.8918
0.2244	192.0011	64848	0.7641	0.9610	0.9086
0.2279	193.0011	65184	0.7270	0.9619	0.9076
0.2267	194.0011	65520	0.7470	0.9601	0.9037
0.2261	195.0010	65856	0.8915	0.9593	0.9044
0.2226	196.0010	66192	0.8146	0.9585	0.8828

Framework versions

Transformers 4.46.0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.1

Arthur-Tsai/histv4_ftis_pretrain_tssp-smlm_0329

histv4_ftis_pretrain_tssp-smlm_0329

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Arthur-Tsai/
histv4_ftis_pretrain_tssp-smlm_0329