receipt-core-model

DanSarm/receipt-core-model

receipt-core-model is a machine learning model from DanSarm. 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. The card lists the license as apache-2.0.

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

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Jan 29, 2025
Updated: Mar 25, 2025
SHA: 13450f91

Model card

From the Hugging Face model README

receipt-core-model

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

Loss: 1.3520

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: 4
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
num_epochs: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
0.1349	1.0	43	0.7831
0.0976	2.0	86	0.8651
0.0793	3.0	129	0.8480
0.0662	4.0	172	0.8880
0.0561	5.0	215	0.9050
0.0488	6.0	258	0.8659
0.0459	7.0	301	0.9210
0.038	8.0	344	0.9130
0.0354	9.0	387	0.9550
0.0334	10.0	430	0.9398
0.0287	11.0	473	1.0235
0.0258	12.0	516	1.0059
0.0235	13.0	559	0.9962
0.0226	14.0	602	0.9741
0.0187	15.0	645	1.0120
0.0175	16.0	688	1.0367
0.0159	17.0	731	1.0202
0.0137	18.0	774	1.0062
0.0131	19.0	817	1.0746
0.0131	20.0	860	1.0623
0.0131	21.0	903	1.0193
0.009	22.0	946	1.0044
0.0084	23.0	989	1.0516
0.0075	24.0	1032	1.0447
0.0081	25.0	1075	1.0559
0.0073	26.0	1118	1.0533
0.007	27.0	1161	1.0439
0.0064	28.0	1204	1.0729
0.0058	29.0	1247	1.0625
0.0051	30.0	1290	1.0582
0.0067	31.0	1333	1.0922
0.0061	32.0	1376	1.1204
0.0052	33.0	1419	1.1241
0.0044	34.0	1462	1.0950
0.005	35.0	1505	1.1386
0.0043	36.0	1548	1.0973
0.0038	37.0	1591	1.1214
0.0029	38.0	1634	1.1272
0.0025	39.0	1677	1.1196
0.0027	40.0	1720	1.1154
0.0025	41.0	1763	1.1367
0.0021	42.0	1806	1.1177
0.0023	43.0	1849	1.1195
0.0023	44.0	1892	1.1280
0.003	45.0	1935	1.0984
0.0034	46.0	1978	1.0901
0.0025	47.0	2021	1.1039
0.0026	48.0	2064	1.1108
0.0015	49.0	2107	1.0975
0.0021	50.0	2150	1.0973
0.0023	51.0	2193	1.1343
0.002	52.0	2236	1.1527
0.0016	53.0	2279	1.1626
0.0017	54.0	2322	1.1769
0.0026	55.0	2365	1.1788
0.0014	56.0	2408	1.2070
0.0012	57.0	2451	1.2130
0.0013	58.0	2494	1.2137
0.0015	59.0	2537	1.2164
0.0013	60.0	2580	1.2188
0.0012	61.0	2623	1.2049
0.0025	62.0	2666	1.1843
0.002	63.0	2709	1.2177
0.0021	64.0	2752	1.2391
0.0014	65.0	2795	1.2096
0.0018	66.0	2838	1.1870
0.001	67.0	2881	1.2115
0.0015	68.0	2924	1.2027
0.0011	69.0	2967	1.2227
0.001	70.0	3010	1.2219
0.0015	71.0	3053	1.2032
0.0014	72.0	3096	1.2110
0.0012	73.0	3139	1.2441
0.0009	74.0	3182	1.2474
0.0007	75.0	3225	1.2519
0.0014	76.0	3268	1.1549
0.0011	77.0	3311	1.1833
0.0008	78.0	3354	1.1899
0.0019	79.0	3397	1.1853
0.0008	80.0	3440	1.1866
0.0016	81.0	3483	1.1632
0.001	82.0	3526	1.1785
0.002	83.0	3569	1.1984
0.0011	84.0	3612	1.1579
0.0012	85.0	3655	1.1529
0.0017	86.0	3698	1.0932
0.0013	87.0	3741	1.1527
0.0008	88.0	3784	1.1480
0.0007	89.0	3827	1.1494
0.0015	90.0	3870	1.1555
0.0007	91.0	3913	1.1515
0.001	92.0	3956	1.1595
0.0009	93.0	3999	1.2398
0.0011	94.0	4042	1.2552
0.0019	95.0	4085	1.2278
0.0015	96.0	4128	1.2063
0.0013	97.0	4171	1.2273
0.001	98.0	4214	1.2248
0.001	99.0	4257	1.2211
0.0009	100.0	4300	1.2223
0.0012	101.0	4343	1.2130
0.001	102.0	4386	1.1942
0.0012	103.0	4429	1.2017
0.0012	104.0	4472	1.2198
0.0033	105.0	4515	1.2088
0.0016	106.0	4558	1.1952
0.0009	107.0	4601	1.2180
0.0008	108.0	4644	1.2303
0.001	109.0	4687	1.2111
0.0021	110.0	4730	1.1927
0.0011	111.0	4773	1.1703
0.0009	112.0	4816	1.2165
0.0007	113.0	4859	1.2007
0.0004	114.0	4902	1.2118
0.0007	115.0	4945	1.2210
0.0006	116.0	4988	1.2366
0.0009	117.0	5031	1.2482
0.0004	118.0	5074	1.2565
0.0004	119.0	5117	1.2601
0.0013	120.0	5160	1.2343
0.0018	121.0	5203	1.2438
0.0015	122.0	5246	1.2380
0.0014	123.0	5289	1.2258
0.0007	124.0	5332	1.2381
0.0008	125.0	5375	1.2372
0.0004	126.0	5418	1.2739
0.0006	127.0	5461	1.2874
0.001	128.0	5504	1.2896
0.0005	129.0	5547	1.3074
0.0011	130.0	5590	1.2632
0.0009	131.0	5633	1.2769
0.0009	132.0	5676	1.2940
0.0006	133.0	5719	1.2697
0.0008	134.0	5762	1.2769
0.0004	135.0	5805	1.2793
0.0003	136.0	5848	1.2738
0.0003	137.0	5891	1.2841
0.0003	138.0	5934	1.2879
0.0003	139.0	5977	1.2734
0.0005	140.0	6020	1.2578
0.0003	141.0	6063	1.2664
0.0002	142.0	6106	1.2691
0.0003	143.0	6149	1.2690
0.0002	144.0	6192	1.2734
0.0002	145.0	6235	1.2947
0.0003	146.0	6278	1.3135
0.0002	147.0	6321	1.3170
0.0006	148.0	6364	1.3229
0.0005	149.0	6407	1.3118
0.001	150.0	6450	1.2621
0.0006	151.0	6493	1.2917
0.0014	152.0	6536	1.3051
0.0011	153.0	6579	1.3440
0.0011	154.0	6622	1.2875
0.0013	155.0	6665	1.2932
0.0014	156.0	6708	1.2724
0.0011	157.0	6751	1.2661
0.0014	158.0	6794	1.2663
0.0019	159.0	6837	1.2867
0.001	160.0	6880	1.3195
0.0007	161.0	6923	1.3343
0.0005	162.0	6966	1.3678
0.0005	163.0	7009	1.3497
0.0006	164.0	7052	1.3562
0.0004	165.0	7095	1.3314
0.0004	166.0	7138	1.3336
0.0003	167.0	7181	1.3237
0.0007	168.0	7224	1.3320
0.0009	169.0	7267	1.3330
0.0003	170.0	7310	1.3295
0.0005	171.0	7353	1.3612
0.0003	172.0	7396	1.3665
0.0008	173.0	7439	1.3701
0.0007	174.0	7482	1.3507
0.0002	175.0	7525	1.3520

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Library: transformers
Type: t5
License: apache-2.0
Created: Jan 29, 2025
Updated: Mar 25, 2025