calculator_model_test

ReadHegel/calculator_model_test

calculator_model_test is a machine learning model from ReadHegel. 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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Hugging Face

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At a glance

Library: transformers
Model type: encoder-decoder
Access: Public
Created: Mar 2, 2026
Updated: Mar 2, 2026
SHA: 7016ad80

Model card

From the Hugging Face model README

calculator_model_test

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

Loss: 0.0006

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: 0.001
train_batch_size: 512
eval_batch_size: 512
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 250

Training results

Training Loss	Epoch	Step	Validation Loss
0.6830	1.0	6	0.3474
0.2905	2.0	12	0.1705
0.1633	3.0	18	0.1282
0.1346	4.0	24	0.1065
0.1074	5.0	30	0.0834
0.1010	6.0	36	0.0615
0.1016	7.0	42	0.0634
0.0770	8.0	48	0.0620
0.0729	9.0	54	0.0903
0.0853	10.0	60	0.0888
0.1005	11.0	66	0.1555
0.1295	12.0	72	0.1269
0.1342	13.0	78	0.0783
0.0937	14.0	84	0.0535
0.0596	15.0	90	0.0485
0.0538	16.0	96	0.0271
0.0479	17.0	102	0.0285
0.0511	18.0	108	0.0340
0.0576	19.0	114	0.0345
0.0502	20.0	120	0.0395
0.0462	21.0	126	0.0349
0.0474	22.0	132	0.0225
0.0341	23.0	138	0.0262
0.0328	24.0	144	0.0279
0.0378	25.0	150	0.0220
0.0421	26.0	156	0.0329
0.0383	27.0	162	0.0240
0.0371	28.0	168	0.0276
0.0297	29.0	174	0.0191
0.0283	30.0	180	0.0153
0.0350	31.0	186	0.0285
0.0348	32.0	192	0.0288
0.0389	33.0	198	0.0555
0.0441	34.0	204	0.0325
0.0454	35.0	210	0.0309
0.0403	36.0	216	0.0220
0.0374	37.0	222	0.0230
0.0395	38.0	228	0.0336
0.0417	39.0	234	0.0222
0.0382	40.0	240	0.0365
0.0341	41.0	246	0.0172
0.0323	42.0	252	0.0187
0.0364	43.0	258	0.0184
0.0388	44.0	264	0.0177
0.0324	45.0	270	0.0125
0.0270	46.0	276	0.0138
0.0270	47.0	282	0.0169
0.0255	48.0	288	0.0147
0.0286	49.0	294	0.0207
0.0255	50.0	300	0.0156
0.0176	51.0	306	0.0191
0.0223	52.0	312	0.0166
0.0174	53.0	318	0.0087
0.0130	54.0	324	0.0078
0.0116	55.0	330	0.0070
0.0097	56.0	336	0.0054
0.0098	57.0	342	0.0048
0.0074	58.0	348	0.0063
0.0081	59.0	354	0.0044
0.0070	60.0	360	0.0042
0.0068	61.0	366	0.0048
0.0065	62.0	372	0.0060
0.0058	63.0	378	0.0037
0.0058	64.0	384	0.0036
0.0049	65.0	390	0.0031
0.0048	66.0	396	0.0034
0.0041	67.0	402	0.0032
0.0041	68.0	408	0.0027
0.0042	69.0	414	0.0029
0.0036	70.0	420	0.0022
0.0039	71.0	426	0.0018
0.0032	72.0	432	0.0031
0.0039	73.0	438	0.0023
0.0050	74.0	444	0.0029
0.0091	75.0	450	0.0039
0.0069	76.0	456	0.0032
0.0080	77.0	462	0.0049
0.0103	78.0	468	0.0066
0.0109	79.0	474	0.0098
0.0082	80.0	480	0.0057
0.0110	81.0	486	0.0051
0.0109	82.0	492	0.0088
0.0175	83.0	498	0.0069
0.0161	84.0	504	0.0120
0.0178	85.0	510	0.0073
0.0188	86.0	516	0.0124
0.0138	87.0	522	0.0041
0.0079	88.0	528	0.0047
0.0089	89.0	534	0.0052
0.0133	90.0	540	0.0319
0.0211	91.0	546	0.0221
0.0208	92.0	552	0.0059
0.0126	93.0	558	0.0101
0.0136	94.0	564	0.0087
0.0103	95.0	570	0.0056
0.0106	96.0	576	0.0053
0.0145	97.0	582	0.0080
0.0125	98.0	588	0.0057
0.0119	99.0	594	0.0066
0.0087	100.0	600	0.0062
0.0088	101.0	606	0.0059
0.0086	102.0	612	0.0045
0.0069	103.0	618	0.0042
0.0098	104.0	624	0.0052
0.0075	105.0	630	0.0039
0.0071	106.0	636	0.0033
0.0107	107.0	642	0.0051
0.0074	108.0	648	0.0054
0.0077	109.0	654	0.0047
0.0120	110.0	660	0.0103
0.0087	111.0	666	0.0031
0.0076	112.0	672	0.0046
0.0061	113.0	678	0.0049
0.0056	114.0	684	0.0031
0.0046	115.0	690	0.0025
0.0044	116.0	696	0.0021
0.0026	117.0	702	0.0019
0.0032	118.0	708	0.0017
0.0062	119.0	714	0.0087
0.0172	120.0	720	0.0040
0.0089	121.0	726	0.0058
0.0091	122.0	732	0.0053
0.0150	123.0	738	0.0026
0.0091	124.0	744	0.0046
0.0113	125.0	750	0.0044
0.0080	126.0	756	0.0046
0.0135	127.0	762	0.0097
0.0224	128.0	768	0.0081
0.0214	129.0	774	0.0107
0.0159	130.0	780	0.0122
0.0179	131.0	786	0.0045
0.0148	132.0	792	0.0074
0.0139	133.0	798	0.0030
0.0072	134.0	804	0.0052
0.0065	135.0	810	0.0029
0.0037	136.0	816	0.0021
0.0031	137.0	822	0.0016
0.0027	138.0	828	0.0015
0.0020	139.0	834	0.0014
0.0028	140.0	840	0.0014
0.0017	141.0	846	0.0014
0.0021	142.0	852	0.0013
0.0020	143.0	858	0.0014
0.0016	144.0	864	0.0013
0.0021	145.0	870	0.0013
0.0015	146.0	876	0.0012
0.0015	147.0	882	0.0011
0.0014	148.0	888	0.0012
0.0014	149.0	894	0.0012
0.0014	150.0	900	0.0012
0.0012	151.0	906	0.0011
0.0018	152.0	912	0.0010
0.0015	153.0	918	0.0009
0.0017	154.0	924	0.0010
0.0024	155.0	930	0.0034
0.0018	156.0	936	0.0013
0.0013	157.0	942	0.0010
0.0012	158.0	948	0.0010
0.0010	159.0	954	0.0010
0.0009	160.0	960	0.0009
0.0008	161.0	966	0.0009
0.0009	162.0	972	0.0009
0.0008	163.0	978	0.0009
0.0007	164.0	984	0.0008
0.0009	165.0	990	0.0008
0.0008	166.0	996	0.0008
0.0009	167.0	1002	0.0009
0.0009	168.0	1008	0.0009
0.0008	169.0	1014	0.0009
0.0012	170.0	1020	0.0009
0.0008	171.0	1026	0.0009
0.0008	172.0	1032	0.0009
0.0007	173.0	1038	0.0008
0.0007	174.0	1044	0.0007
0.0006	175.0	1050	0.0007
0.0007	176.0	1056	0.0007
0.0005	177.0	1062	0.0007
0.0006	178.0	1068	0.0007
0.0007	179.0	1074	0.0007
0.0005	180.0	1080	0.0007
0.0006	181.0	1086	0.0007
0.0005	182.0	1092	0.0006
0.0005	183.0	1098	0.0006
0.0005	184.0	1104	0.0006
0.0007	185.0	1110	0.0006
0.0005	186.0	1116	0.0007
0.0006	187.0	1122	0.0007
0.0005	188.0	1128	0.0007
0.0005	189.0	1134	0.0007
0.0006	190.0	1140	0.0007
0.0006	191.0	1146	0.0007
0.0005	192.0	1152	0.0007
0.0004	193.0	1158	0.0007
0.0004	194.0	1164	0.0007
0.0006	195.0	1170	0.0007
0.0006	196.0	1176	0.0007
0.0004	197.0	1182	0.0007
0.0006	198.0	1188	0.0007
0.0006	199.0	1194	0.0007
0.0005	200.0	1200	0.0007
0.0006	201.0	1206	0.0007
0.0005	202.0	1212	0.0006
0.0005	203.0	1218	0.0006
0.0006	204.0	1224	0.0007
0.0004	205.0	1230	0.0007
0.0004	206.0	1236	0.0007
0.0006	207.0	1242	0.0007
0.0004	208.0	1248	0.0007
0.0005	209.0	1254	0.0006
0.0004	210.0	1260	0.0006
0.0004	211.0	1266	0.0006
0.0004	212.0	1272	0.0006
0.0004	213.0	1278	0.0006
0.0005	214.0	1284	0.0006
0.0005	215.0	1290	0.0006
0.0004	216.0	1296	0.0006
0.0005	217.0	1302	0.0006
0.0005	218.0	1308	0.0006
0.0005	219.0	1314	0.0006
0.0004	220.0	1320	0.0006
0.0004	221.0	1326	0.0006
0.0004	222.0	1332	0.0006
0.0005	223.0	1338	0.0006
0.0004	224.0	1344	0.0005
0.0004	225.0	1350	0.0006
0.0003	226.0	1356	0.0006
0.0004	227.0	1362	0.0006
0.0004	228.0	1368	0.0006
0.0004	229.0	1374	0.0006
0.0005	230.0	1380	0.0006
0.0005	231.0	1386	0.0006
0.0004	232.0	1392	0.0006
0.0005	233.0	1398	0.0006
0.0004	234.0	1404	0.0006
0.0004	235.0	1410	0.0006
0.0005	236.0	1416	0.0006
0.0004	237.0	1422	0.0006
0.0004	238.0	1428	0.0006
0.0003	239.0	1434	0.0006
0.0004	240.0	1440	0.0006
0.0006	241.0	1446	0.0006
0.0003	242.0	1452	0.0006
0.0003	243.0	1458	0.0006
0.0005	244.0	1464	0.0006
0.0003	245.0	1470	0.0006
0.0004	246.0	1476	0.0006
0.0004	247.0	1482	0.0006
0.0005	248.0	1488	0.0006
0.0006	249.0	1494	0.0006
0.0004	250.0	1500	0.0006

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Library: transformers
Type: encoder-decoder
Created: Mar 2, 2026
Updated: Mar 2, 2026