DanSarm commited on
Commit
13450f9
·
verified ·
1 Parent(s): 056de2e

Training complete!

Browse files
Files changed (3) hide show
  1. README.md +178 -104
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
16
 
17
  This model is a fine-tuned version of [DanSarm/receipt-core-model](https://huggingface.co/DanSarm/receipt-core-model) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 1.2194
20
 
21
  ## Model description
22
 
@@ -47,112 +47,186 @@ The following hyperparameters were used during training:
47
 
48
  | Training Loss | Epoch | Step | Validation Loss |
49
  |:-------------:|:-----:|:----:|:---------------:|
50
- | 0.1872 | 1.0 | 36 | 0.8766 |
51
- | 0.1235 | 2.0 | 72 | 0.9059 |
52
- | 0.0904 | 3.0 | 108 | 0.9360 |
53
- | 0.0762 | 4.0 | 144 | 0.8768 |
54
- | 0.0652 | 5.0 | 180 | 0.9361 |
55
- | 0.054 | 6.0 | 216 | 0.9305 |
56
- | 0.047 | 7.0 | 252 | 0.9453 |
57
- | 0.0427 | 8.0 | 288 | 1.0083 |
58
- | 0.0375 | 9.0 | 324 | 1.0142 |
59
- | 0.0317 | 10.0 | 360 | 1.0458 |
60
- | 0.0303 | 11.0 | 396 | 1.0515 |
61
- | 0.0283 | 12.0 | 432 | 1.0791 |
62
- | 0.0259 | 13.0 | 468 | 1.0594 |
63
- | 0.0236 | 14.0 | 504 | 1.1078 |
64
- | 0.0213 | 15.0 | 540 | 1.0250 |
65
- | 0.0194 | 16.0 | 576 | 1.0492 |
66
- | 0.0158 | 17.0 | 612 | 1.0782 |
67
- | 0.016 | 18.0 | 648 | 1.1181 |
68
- | 0.0135 | 19.0 | 684 | 1.1222 |
69
- | 0.0138 | 20.0 | 720 | 1.1314 |
70
- | 0.013 | 21.0 | 756 | 1.1197 |
71
- | 0.0106 | 22.0 | 792 | 1.1216 |
72
- | 0.0106 | 23.0 | 828 | 1.1382 |
73
- | 0.0105 | 24.0 | 864 | 1.1542 |
74
- | 0.0084 | 25.0 | 900 | 1.1758 |
75
- | 0.0078 | 26.0 | 936 | 1.1630 |
76
- | 0.0071 | 27.0 | 972 | 1.1524 |
77
- | 0.007 | 28.0 | 1008 | 1.1615 |
78
- | 0.0049 | 29.0 | 1044 | 1.1673 |
79
- | 0.0062 | 30.0 | 1080 | 1.1623 |
80
- | 0.0057 | 31.0 | 1116 | 1.1709 |
81
- | 0.0046 | 32.0 | 1152 | 1.1976 |
82
- | 0.0043 | 33.0 | 1188 | 1.2217 |
83
- | 0.0035 | 34.0 | 1224 | 1.1863 |
84
- | 0.0051 | 35.0 | 1260 | 1.2208 |
85
- | 0.006 | 36.0 | 1296 | 1.1681 |
86
- | 0.0044 | 37.0 | 1332 | 1.1783 |
87
- | 0.0053 | 38.0 | 1368 | 1.1821 |
88
- | 0.0049 | 39.0 | 1404 | 1.1724 |
89
- | 0.0042 | 40.0 | 1440 | 1.1936 |
90
- | 0.0031 | 41.0 | 1476 | 1.2066 |
91
- | 0.0031 | 42.0 | 1512 | 1.2156 |
92
- | 0.0039 | 43.0 | 1548 | 1.2054 |
93
- | 0.0026 | 44.0 | 1584 | 1.2000 |
94
- | 0.0028 | 45.0 | 1620 | 1.2259 |
95
- | 0.0021 | 46.0 | 1656 | 1.2244 |
96
- | 0.0026 | 47.0 | 1692 | 1.2218 |
97
- | 0.0037 | 48.0 | 1728 | 1.2165 |
98
- | 0.003 | 49.0 | 1764 | 1.2012 |
99
- | 0.0021 | 50.0 | 1800 | 1.1950 |
100
- | 0.0026 | 51.0 | 1836 | 1.2444 |
101
- | 0.0024 | 52.0 | 1872 | 1.2066 |
102
- | 0.0023 | 53.0 | 1908 | 1.2075 |
103
- | 0.002 | 54.0 | 1944 | 1.2476 |
104
- | 0.0016 | 55.0 | 1980 | 1.2365 |
105
- | 0.0016 | 56.0 | 2016 | 1.2422 |
106
- | 0.0014 | 57.0 | 2052 | 1.2420 |
107
- | 0.0013 | 58.0 | 2088 | 1.2246 |
108
- | 0.002 | 59.0 | 2124 | 1.2482 |
109
- | 0.0014 | 60.0 | 2160 | 1.2752 |
110
- | 0.0014 | 61.0 | 2196 | 1.2494 |
111
- | 0.0013 | 62.0 | 2232 | 1.2648 |
112
- | 0.0018 | 63.0 | 2268 | 1.2743 |
113
- | 0.0027 | 64.0 | 2304 | 1.2162 |
114
- | 0.0019 | 65.0 | 2340 | 1.2315 |
115
- | 0.0016 | 66.0 | 2376 | 1.2573 |
116
- | 0.0012 | 67.0 | 2412 | 1.2511 |
117
- | 0.0018 | 68.0 | 2448 | 1.2632 |
118
- | 0.0022 | 69.0 | 2484 | 1.2582 |
119
- | 0.0015 | 70.0 | 2520 | 1.2676 |
120
- | 0.0011 | 71.0 | 2556 | 1.2798 |
121
- | 0.002 | 72.0 | 2592 | 1.2352 |
122
- | 0.0012 | 73.0 | 2628 | 1.2430 |
123
- | 0.0012 | 74.0 | 2664 | 1.2731 |
124
- | 0.001 | 75.0 | 2700 | 1.2773 |
125
- | 0.0009 | 76.0 | 2736 | 1.2506 |
126
- | 0.001 | 77.0 | 2772 | 1.2479 |
127
- | 0.0008 | 78.0 | 2808 | 1.2521 |
128
- | 0.0008 | 79.0 | 2844 | 1.2630 |
129
- | 0.0005 | 80.0 | 2880 | 1.2725 |
130
- | 0.0009 | 81.0 | 2916 | 1.2539 |
131
- | 0.0005 | 82.0 | 2952 | 1.2643 |
132
- | 0.0007 | 83.0 | 2988 | 1.2722 |
133
- | 0.001 | 84.0 | 3024 | 1.2690 |
134
- | 0.0007 | 85.0 | 3060 | 1.2914 |
135
- | 0.0006 | 86.0 | 3096 | 1.2911 |
136
- | 0.0007 | 87.0 | 3132 | 1.2977 |
137
- | 0.0007 | 88.0 | 3168 | 1.3432 |
138
- | 0.0008 | 89.0 | 3204 | 1.3392 |
139
- | 0.001 | 90.0 | 3240 | 1.2964 |
140
- | 0.0023 | 91.0 | 3276 | 1.2660 |
141
- | 0.0019 | 92.0 | 3312 | 1.2739 |
142
- | 0.0017 | 93.0 | 3348 | 1.2968 |
143
- | 0.0017 | 94.0 | 3384 | 1.3048 |
144
- | 0.0014 | 95.0 | 3420 | 1.3139 |
145
- | 0.0017 | 96.0 | 3456 | 1.3031 |
146
- | 0.0012 | 97.0 | 3492 | 1.2952 |
147
- | 0.0014 | 98.0 | 3528 | 1.3281 |
148
- | 0.0021 | 99.0 | 3564 | 1.3087 |
149
- | 0.0024 | 100.0 | 3600 | 1.2122 |
150
- | 0.0028 | 101.0 | 3636 | 1.2194 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
151
 
152
 
153
  ### Framework versions
154
 
155
  - Transformers 4.49.0
156
  - Pytorch 2.6.0+cu124
157
- - Datasets 3.3.1
158
- - Tokenizers 0.21.0
 
16
 
17
  This model is a fine-tuned version of [DanSarm/receipt-core-model](https://huggingface.co/DanSarm/receipt-core-model) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 1.3520
20
 
21
  ## Model description
22
 
 
47
 
48
  | Training Loss | Epoch | Step | Validation Loss |
49
  |:-------------:|:-----:|:----:|:---------------:|
50
+ | 0.1349 | 1.0 | 43 | 0.7831 |
51
+ | 0.0976 | 2.0 | 86 | 0.8651 |
52
+ | 0.0793 | 3.0 | 129 | 0.8480 |
53
+ | 0.0662 | 4.0 | 172 | 0.8880 |
54
+ | 0.0561 | 5.0 | 215 | 0.9050 |
55
+ | 0.0488 | 6.0 | 258 | 0.8659 |
56
+ | 0.0459 | 7.0 | 301 | 0.9210 |
57
+ | 0.038 | 8.0 | 344 | 0.9130 |
58
+ | 0.0354 | 9.0 | 387 | 0.9550 |
59
+ | 0.0334 | 10.0 | 430 | 0.9398 |
60
+ | 0.0287 | 11.0 | 473 | 1.0235 |
61
+ | 0.0258 | 12.0 | 516 | 1.0059 |
62
+ | 0.0235 | 13.0 | 559 | 0.9962 |
63
+ | 0.0226 | 14.0 | 602 | 0.9741 |
64
+ | 0.0187 | 15.0 | 645 | 1.0120 |
65
+ | 0.0175 | 16.0 | 688 | 1.0367 |
66
+ | 0.0159 | 17.0 | 731 | 1.0202 |
67
+ | 0.0137 | 18.0 | 774 | 1.0062 |
68
+ | 0.0131 | 19.0 | 817 | 1.0746 |
69
+ | 0.0131 | 20.0 | 860 | 1.0623 |
70
+ | 0.0131 | 21.0 | 903 | 1.0193 |
71
+ | 0.009 | 22.0 | 946 | 1.0044 |
72
+ | 0.0084 | 23.0 | 989 | 1.0516 |
73
+ | 0.0075 | 24.0 | 1032 | 1.0447 |
74
+ | 0.0081 | 25.0 | 1075 | 1.0559 |
75
+ | 0.0073 | 26.0 | 1118 | 1.0533 |
76
+ | 0.007 | 27.0 | 1161 | 1.0439 |
77
+ | 0.0064 | 28.0 | 1204 | 1.0729 |
78
+ | 0.0058 | 29.0 | 1247 | 1.0625 |
79
+ | 0.0051 | 30.0 | 1290 | 1.0582 |
80
+ | 0.0067 | 31.0 | 1333 | 1.0922 |
81
+ | 0.0061 | 32.0 | 1376 | 1.1204 |
82
+ | 0.0052 | 33.0 | 1419 | 1.1241 |
83
+ | 0.0044 | 34.0 | 1462 | 1.0950 |
84
+ | 0.005 | 35.0 | 1505 | 1.1386 |
85
+ | 0.0043 | 36.0 | 1548 | 1.0973 |
86
+ | 0.0038 | 37.0 | 1591 | 1.1214 |
87
+ | 0.0029 | 38.0 | 1634 | 1.1272 |
88
+ | 0.0025 | 39.0 | 1677 | 1.1196 |
89
+ | 0.0027 | 40.0 | 1720 | 1.1154 |
90
+ | 0.0025 | 41.0 | 1763 | 1.1367 |
91
+ | 0.0021 | 42.0 | 1806 | 1.1177 |
92
+ | 0.0023 | 43.0 | 1849 | 1.1195 |
93
+ | 0.0023 | 44.0 | 1892 | 1.1280 |
94
+ | 0.003 | 45.0 | 1935 | 1.0984 |
95
+ | 0.0034 | 46.0 | 1978 | 1.0901 |
96
+ | 0.0025 | 47.0 | 2021 | 1.1039 |
97
+ | 0.0026 | 48.0 | 2064 | 1.1108 |
98
+ | 0.0015 | 49.0 | 2107 | 1.0975 |
99
+ | 0.0021 | 50.0 | 2150 | 1.0973 |
100
+ | 0.0023 | 51.0 | 2193 | 1.1343 |
101
+ | 0.002 | 52.0 | 2236 | 1.1527 |
102
+ | 0.0016 | 53.0 | 2279 | 1.1626 |
103
+ | 0.0017 | 54.0 | 2322 | 1.1769 |
104
+ | 0.0026 | 55.0 | 2365 | 1.1788 |
105
+ | 0.0014 | 56.0 | 2408 | 1.2070 |
106
+ | 0.0012 | 57.0 | 2451 | 1.2130 |
107
+ | 0.0013 | 58.0 | 2494 | 1.2137 |
108
+ | 0.0015 | 59.0 | 2537 | 1.2164 |
109
+ | 0.0013 | 60.0 | 2580 | 1.2188 |
110
+ | 0.0012 | 61.0 | 2623 | 1.2049 |
111
+ | 0.0025 | 62.0 | 2666 | 1.1843 |
112
+ | 0.002 | 63.0 | 2709 | 1.2177 |
113
+ | 0.0021 | 64.0 | 2752 | 1.2391 |
114
+ | 0.0014 | 65.0 | 2795 | 1.2096 |
115
+ | 0.0018 | 66.0 | 2838 | 1.1870 |
116
+ | 0.001 | 67.0 | 2881 | 1.2115 |
117
+ | 0.0015 | 68.0 | 2924 | 1.2027 |
118
+ | 0.0011 | 69.0 | 2967 | 1.2227 |
119
+ | 0.001 | 70.0 | 3010 | 1.2219 |
120
+ | 0.0015 | 71.0 | 3053 | 1.2032 |
121
+ | 0.0014 | 72.0 | 3096 | 1.2110 |
122
+ | 0.0012 | 73.0 | 3139 | 1.2441 |
123
+ | 0.0009 | 74.0 | 3182 | 1.2474 |
124
+ | 0.0007 | 75.0 | 3225 | 1.2519 |
125
+ | 0.0014 | 76.0 | 3268 | 1.1549 |
126
+ | 0.0011 | 77.0 | 3311 | 1.1833 |
127
+ | 0.0008 | 78.0 | 3354 | 1.1899 |
128
+ | 0.0019 | 79.0 | 3397 | 1.1853 |
129
+ | 0.0008 | 80.0 | 3440 | 1.1866 |
130
+ | 0.0016 | 81.0 | 3483 | 1.1632 |
131
+ | 0.001 | 82.0 | 3526 | 1.1785 |
132
+ | 0.002 | 83.0 | 3569 | 1.1984 |
133
+ | 0.0011 | 84.0 | 3612 | 1.1579 |
134
+ | 0.0012 | 85.0 | 3655 | 1.1529 |
135
+ | 0.0017 | 86.0 | 3698 | 1.0932 |
136
+ | 0.0013 | 87.0 | 3741 | 1.1527 |
137
+ | 0.0008 | 88.0 | 3784 | 1.1480 |
138
+ | 0.0007 | 89.0 | 3827 | 1.1494 |
139
+ | 0.0015 | 90.0 | 3870 | 1.1555 |
140
+ | 0.0007 | 91.0 | 3913 | 1.1515 |
141
+ | 0.001 | 92.0 | 3956 | 1.1595 |
142
+ | 0.0009 | 93.0 | 3999 | 1.2398 |
143
+ | 0.0011 | 94.0 | 4042 | 1.2552 |
144
+ | 0.0019 | 95.0 | 4085 | 1.2278 |
145
+ | 0.0015 | 96.0 | 4128 | 1.2063 |
146
+ | 0.0013 | 97.0 | 4171 | 1.2273 |
147
+ | 0.001 | 98.0 | 4214 | 1.2248 |
148
+ | 0.001 | 99.0 | 4257 | 1.2211 |
149
+ | 0.0009 | 100.0 | 4300 | 1.2223 |
150
+ | 0.0012 | 101.0 | 4343 | 1.2130 |
151
+ | 0.001 | 102.0 | 4386 | 1.1942 |
152
+ | 0.0012 | 103.0 | 4429 | 1.2017 |
153
+ | 0.0012 | 104.0 | 4472 | 1.2198 |
154
+ | 0.0033 | 105.0 | 4515 | 1.2088 |
155
+ | 0.0016 | 106.0 | 4558 | 1.1952 |
156
+ | 0.0009 | 107.0 | 4601 | 1.2180 |
157
+ | 0.0008 | 108.0 | 4644 | 1.2303 |
158
+ | 0.001 | 109.0 | 4687 | 1.2111 |
159
+ | 0.0021 | 110.0 | 4730 | 1.1927 |
160
+ | 0.0011 | 111.0 | 4773 | 1.1703 |
161
+ | 0.0009 | 112.0 | 4816 | 1.2165 |
162
+ | 0.0007 | 113.0 | 4859 | 1.2007 |
163
+ | 0.0004 | 114.0 | 4902 | 1.2118 |
164
+ | 0.0007 | 115.0 | 4945 | 1.2210 |
165
+ | 0.0006 | 116.0 | 4988 | 1.2366 |
166
+ | 0.0009 | 117.0 | 5031 | 1.2482 |
167
+ | 0.0004 | 118.0 | 5074 | 1.2565 |
168
+ | 0.0004 | 119.0 | 5117 | 1.2601 |
169
+ | 0.0013 | 120.0 | 5160 | 1.2343 |
170
+ | 0.0018 | 121.0 | 5203 | 1.2438 |
171
+ | 0.0015 | 122.0 | 5246 | 1.2380 |
172
+ | 0.0014 | 123.0 | 5289 | 1.2258 |
173
+ | 0.0007 | 124.0 | 5332 | 1.2381 |
174
+ | 0.0008 | 125.0 | 5375 | 1.2372 |
175
+ | 0.0004 | 126.0 | 5418 | 1.2739 |
176
+ | 0.0006 | 127.0 | 5461 | 1.2874 |
177
+ | 0.001 | 128.0 | 5504 | 1.2896 |
178
+ | 0.0005 | 129.0 | 5547 | 1.3074 |
179
+ | 0.0011 | 130.0 | 5590 | 1.2632 |
180
+ | 0.0009 | 131.0 | 5633 | 1.2769 |
181
+ | 0.0009 | 132.0 | 5676 | 1.2940 |
182
+ | 0.0006 | 133.0 | 5719 | 1.2697 |
183
+ | 0.0008 | 134.0 | 5762 | 1.2769 |
184
+ | 0.0004 | 135.0 | 5805 | 1.2793 |
185
+ | 0.0003 | 136.0 | 5848 | 1.2738 |
186
+ | 0.0003 | 137.0 | 5891 | 1.2841 |
187
+ | 0.0003 | 138.0 | 5934 | 1.2879 |
188
+ | 0.0003 | 139.0 | 5977 | 1.2734 |
189
+ | 0.0005 | 140.0 | 6020 | 1.2578 |
190
+ | 0.0003 | 141.0 | 6063 | 1.2664 |
191
+ | 0.0002 | 142.0 | 6106 | 1.2691 |
192
+ | 0.0003 | 143.0 | 6149 | 1.2690 |
193
+ | 0.0002 | 144.0 | 6192 | 1.2734 |
194
+ | 0.0002 | 145.0 | 6235 | 1.2947 |
195
+ | 0.0003 | 146.0 | 6278 | 1.3135 |
196
+ | 0.0002 | 147.0 | 6321 | 1.3170 |
197
+ | 0.0006 | 148.0 | 6364 | 1.3229 |
198
+ | 0.0005 | 149.0 | 6407 | 1.3118 |
199
+ | 0.001 | 150.0 | 6450 | 1.2621 |
200
+ | 0.0006 | 151.0 | 6493 | 1.2917 |
201
+ | 0.0014 | 152.0 | 6536 | 1.3051 |
202
+ | 0.0011 | 153.0 | 6579 | 1.3440 |
203
+ | 0.0011 | 154.0 | 6622 | 1.2875 |
204
+ | 0.0013 | 155.0 | 6665 | 1.2932 |
205
+ | 0.0014 | 156.0 | 6708 | 1.2724 |
206
+ | 0.0011 | 157.0 | 6751 | 1.2661 |
207
+ | 0.0014 | 158.0 | 6794 | 1.2663 |
208
+ | 0.0019 | 159.0 | 6837 | 1.2867 |
209
+ | 0.001 | 160.0 | 6880 | 1.3195 |
210
+ | 0.0007 | 161.0 | 6923 | 1.3343 |
211
+ | 0.0005 | 162.0 | 6966 | 1.3678 |
212
+ | 0.0005 | 163.0 | 7009 | 1.3497 |
213
+ | 0.0006 | 164.0 | 7052 | 1.3562 |
214
+ | 0.0004 | 165.0 | 7095 | 1.3314 |
215
+ | 0.0004 | 166.0 | 7138 | 1.3336 |
216
+ | 0.0003 | 167.0 | 7181 | 1.3237 |
217
+ | 0.0007 | 168.0 | 7224 | 1.3320 |
218
+ | 0.0009 | 169.0 | 7267 | 1.3330 |
219
+ | 0.0003 | 170.0 | 7310 | 1.3295 |
220
+ | 0.0005 | 171.0 | 7353 | 1.3612 |
221
+ | 0.0003 | 172.0 | 7396 | 1.3665 |
222
+ | 0.0008 | 173.0 | 7439 | 1.3701 |
223
+ | 0.0007 | 174.0 | 7482 | 1.3507 |
224
+ | 0.0002 | 175.0 | 7525 | 1.3520 |
225
 
226
 
227
  ### Framework versions
228
 
229
  - Transformers 4.49.0
230
  - Pytorch 2.6.0+cu124
231
+ - Datasets 3.4.1
232
+ - Tokenizers 0.21.1
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:5c9701fd74590ae0cb21086d946f5fb645fee1f3b13d53fe0019abc2af87aac5
3
  size 891644712
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8c5c32c074bccb94604a7c967917c72aaa1db4c005f45db9a3a41378ab60c468
3
  size 891644712
training_args.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:ab659f0503373faf6a5e1d23770209e5dfe4422179d6514ce77b0cc7d04f58ec
3
  size 5496
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0ac317b567dfc78588e2c515ddf40c7289f50fd90ebd49e9df8f295caddc20d0
3
  size 5496