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finerweb-binary-classifier-mdeberta-gemma3

Text Classification
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deberta-v2
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text-embeddings-inference
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@@ -21,10 +21,6 @@ should probably proofread and complete it, then remove this comment. -->
21
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
22
  It achieves the following results on the evaluation set:
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  - Loss: 0.1614
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- - Precision: 1.0
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- - Recall: 1.0
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- - F1 Macro: 1.0
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- - Accuracy: 1.0
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  ## Model description
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@@ -53,111 +49,111 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
57
- |:-------------:|:-------:|:------:|:---------------:|:---------:|:------:|:--------:|:--------:|
58
- | No log | 0 | 0 | 0.2170 | 1.0 | 1.0 | 1.0 | 1.0 |
59
- | 0.1238 | 0.1954 | 1000 | 0.1458 | 1.0 | 1.0 | 1.0 | 1.0 |
60
- | 0.1143 | 0.3908 | 2000 | 0.1076 | 1.0 | 1.0 | 1.0 | 1.0 |
61
- | 0.1208 | 0.5862 | 3000 | 0.1133 | 1.0 | 1.0 | 1.0 | 1.0 |
62
- | 0.1031 | 0.7816 | 4000 | 0.1069 | 1.0 | 1.0 | 1.0 | 1.0 |
63
- | 0.1045 | 0.9769 | 5000 | 0.1047 | 1.0 | 1.0 | 1.0 | 1.0 |
64
- | 0.0974 | 1.1723 | 6000 | 0.1087 | 1.0 | 1.0 | 1.0 | 1.0 |
65
- | 0.1337 | 1.3677 | 7000 | 0.1209 | 1.0 | 1.0 | 1.0 | 1.0 |
66
- | 0.0864 | 1.5631 | 8000 | 0.1151 | 1.0 | 1.0 | 1.0 | 1.0 |
67
- | 0.1049 | 1.7585 | 9000 | 0.1203 | 1.0 | 1.0 | 1.0 | 1.0 |
68
- | 0.0915 | 1.9539 | 10000 | 0.1255 | 1.0 | 1.0 | 1.0 | 1.0 |
69
- | 0.079 | 2.1493 | 11000 | 0.1369 | 1.0 | 1.0 | 1.0 | 1.0 |
70
- | 0.0709 | 2.3447 | 12000 | 0.1167 | 1.0 | 1.0 | 1.0 | 1.0 |
71
- | 0.0823 | 2.5401 | 13000 | 0.1257 | 1.0 | 1.0 | 1.0 | 1.0 |
72
- | 0.0725 | 2.7354 | 14000 | 0.1114 | 1.0 | 1.0 | 1.0 | 1.0 |
73
- | 0.066 | 2.9308 | 15000 | 0.1232 | 1.0 | 1.0 | 1.0 | 1.0 |
74
- | 0.0354 | 3.1262 | 16000 | 0.1321 | 1.0 | 1.0 | 1.0 | 1.0 |
75
- | 0.0486 | 3.3216 | 17000 | 0.1245 | 1.0 | 1.0 | 1.0 | 1.0 |
76
- | 0.0466 | 3.5170 | 18000 | 0.1329 | 1.0 | 1.0 | 1.0 | 1.0 |
77
- | 0.0523 | 3.7124 | 19000 | 0.1350 | 1.0 | 1.0 | 1.0 | 1.0 |
78
- | 0.0614 | 3.9078 | 20000 | 0.1373 | 1.0 | 1.0 | 1.0 | 1.0 |
79
- | 0.0349 | 4.1032 | 21000 | 0.1461 | 1.0 | 1.0 | 1.0 | 1.0 |
80
- | 0.0315 | 4.2986 | 22000 | 0.1406 | 1.0 | 1.0 | 1.0 | 1.0 |
81
- | 0.0337 | 4.4939 | 23000 | 0.1355 | 1.0 | 1.0 | 1.0 | 1.0 |
82
- | 0.0345 | 4.6893 | 24000 | 0.1300 | 1.0 | 1.0 | 1.0 | 1.0 |
83
- | 0.0324 | 4.8847 | 25000 | 0.1324 | 1.0 | 1.0 | 1.0 | 1.0 |
84
- | 0.0247 | 5.0801 | 26000 | 0.1347 | 1.0 | 1.0 | 1.0 | 1.0 |
85
- | 0.0306 | 5.2755 | 27000 | 0.1474 | 1.0 | 1.0 | 1.0 | 1.0 |
86
- | 0.0267 | 5.4709 | 28000 | 0.1394 | 1.0 | 1.0 | 1.0 | 1.0 |
87
- | 0.0204 | 5.6663 | 29000 | 0.1487 | 1.0 | 1.0 | 1.0 | 1.0 |
88
- | 0.0266 | 5.8617 | 30000 | 0.1454 | 1.0 | 1.0 | 1.0 | 1.0 |
89
- | 0.0188 | 6.0571 | 31000 | 0.1434 | 1.0 | 1.0 | 1.0 | 1.0 |
90
- | 0.0192 | 6.2524 | 32000 | 0.1458 | 1.0 | 1.0 | 1.0 | 1.0 |
91
- | 0.0246 | 6.4478 | 33000 | 0.1455 | 1.0 | 1.0 | 1.0 | 1.0 |
92
- | 0.0219 | 6.6432 | 34000 | 0.1440 | 1.0 | 1.0 | 1.0 | 1.0 |
93
- | 0.0231 | 6.8386 | 35000 | 0.1561 | 1.0 | 1.0 | 1.0 | 1.0 |
94
- | 0.0185 | 7.0340 | 36000 | 0.1504 | 1.0 | 1.0 | 1.0 | 1.0 |
95
- | 0.0163 | 7.2294 | 37000 | 0.1516 | 1.0 | 1.0 | 1.0 | 1.0 |
96
- | 0.0186 | 7.4248 | 38000 | 0.1451 | 1.0 | 1.0 | 1.0 | 1.0 |
97
- | 0.025 | 7.6202 | 39000 | 0.1423 | 1.0 | 1.0 | 1.0 | 1.0 |
98
- | 0.0217 | 7.8156 | 40000 | 0.1453 | 1.0 | 1.0 | 1.0 | 1.0 |
99
- | 0.0226 | 8.0109 | 41000 | 0.1584 | 1.0 | 1.0 | 1.0 | 1.0 |
100
- | 0.0136 | 8.2063 | 42000 | 0.1589 | 1.0 | 1.0 | 1.0 | 1.0 |
101
- | 0.0186 | 8.4017 | 43000 | 0.1518 | 1.0 | 1.0 | 1.0 | 1.0 |
102
- | 0.0215 | 8.5971 | 44000 | 0.1459 | 1.0 | 1.0 | 1.0 | 1.0 |
103
- | 0.0182 | 8.7925 | 45000 | 0.1437 | 1.0 | 1.0 | 1.0 | 1.0 |
104
- | 0.0236 | 8.9879 | 46000 | 0.1409 | 1.0 | 1.0 | 1.0 | 1.0 |
105
- | 0.0204 | 9.1833 | 47000 | 0.1514 | 1.0 | 1.0 | 1.0 | 1.0 |
106
- | 0.0198 | 9.3787 | 48000 | 0.1617 | 1.0 | 1.0 | 1.0 | 1.0 |
107
- | 0.0158 | 9.5741 | 49000 | 0.1366 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0256 | 9.7694 | 50000 | 0.1450 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0186 | 9.9648 | 51000 | 0.1525 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0238 | 10.1602 | 52000 | 0.1658 | 1.0 | 1.0 | 1.0 | 1.0 |
111
- | 0.0189 | 10.3556 | 53000 | 0.1442 | 1.0 | 1.0 | 1.0 | 1.0 |
112
- | 0.0184 | 10.5510 | 54000 | 0.1495 | 1.0 | 1.0 | 1.0 | 1.0 |
113
- | 0.0196 | 10.7464 | 55000 | 0.1428 | 1.0 | 1.0 | 1.0 | 1.0 |
114
- | 0.0224 | 10.9418 | 56000 | 0.1606 | 1.0 | 1.0 | 1.0 | 1.0 |
115
- | 0.018 | 11.1372 | 57000 | 0.1436 | 1.0 | 1.0 | 1.0 | 1.0 |
116
- | 0.1813 | 11.3326 | 58000 | 0.1829 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.1888 | 11.5279 | 59000 | 0.1832 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.1826 | 11.7233 | 60000 | 0.1828 | 1.0 | 1.0 | 1.0 | 1.0 |
119
- | 0.0227 | 11.9187 | 61000 | 0.1447 | 1.0 | 1.0 | 1.0 | 1.0 |
120
- | 0.0162 | 12.1141 | 62000 | 0.1491 | 1.0 | 1.0 | 1.0 | 1.0 |
121
- | 0.0212 | 12.3095 | 63000 | 0.1386 | 1.0 | 1.0 | 1.0 | 1.0 |
122
- | 0.0124 | 12.5049 | 64000 | 0.1507 | 1.0 | 1.0 | 1.0 | 1.0 |
123
- | 0.0162 | 12.7003 | 65000 | 0.1425 | 1.0 | 1.0 | 1.0 | 1.0 |
124
- | 0.0122 | 12.8957 | 66000 | 0.1417 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0105 | 13.0911 | 67000 | 0.1414 | 1.0 | 1.0 | 1.0 | 1.0 |
126
- | 0.0114 | 13.2864 | 68000 | 0.1537 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0083 | 13.4818 | 69000 | 0.1548 | 1.0 | 1.0 | 1.0 | 1.0 |
128
- | 0.0084 | 13.6772 | 70000 | 0.1439 | 1.0 | 1.0 | 1.0 | 1.0 |
129
- | 0.0113 | 13.8726 | 71000 | 0.1504 | 1.0 | 1.0 | 1.0 | 1.0 |
130
- | 0.013 | 14.0680 | 72000 | 0.1480 | 1.0 | 1.0 | 1.0 | 1.0 |
131
- | 0.0066 | 14.2634 | 73000 | 0.1544 | 1.0 | 1.0 | 1.0 | 1.0 |
132
- | 0.0119 | 14.4588 | 74000 | 0.1509 | 1.0 | 1.0 | 1.0 | 1.0 |
133
- | 0.0078 | 14.6542 | 75000 | 0.1546 | 1.0 | 1.0 | 1.0 | 1.0 |
134
- | 0.0162 | 14.8496 | 76000 | 0.1513 | 1.0 | 1.0 | 1.0 | 1.0 |
135
- | 0.0094 | 15.0449 | 77000 | 0.1571 | 1.0 | 1.0 | 1.0 | 1.0 |
136
- | 0.0097 | 15.2403 | 78000 | 0.1646 | 1.0 | 1.0 | 1.0 | 1.0 |
137
- | 0.0132 | 15.4357 | 79000 | 0.1505 | 1.0 | 1.0 | 1.0 | 1.0 |
138
- | 0.0127 | 15.6311 | 80000 | 0.1539 | 1.0 | 1.0 | 1.0 | 1.0 |
139
- | 0.0086 | 15.8265 | 81000 | 0.1572 | 1.0 | 1.0 | 1.0 | 1.0 |
140
- | 0.0067 | 16.0219 | 82000 | 0.1583 | 1.0 | 1.0 | 1.0 | 1.0 |
141
- | 0.007 | 16.2173 | 83000 | 0.1531 | 1.0 | 1.0 | 1.0 | 1.0 |
142
- | 0.0117 | 16.4127 | 84000 | 0.1485 | 1.0 | 1.0 | 1.0 | 1.0 |
143
- | 0.0156 | 16.6081 | 85000 | 0.1495 | 1.0 | 1.0 | 1.0 | 1.0 |
144
- | 0.0089 | 16.8034 | 86000 | 0.1570 | 1.0 | 1.0 | 1.0 | 1.0 |
145
- | 0.0075 | 16.9988 | 87000 | 0.1540 | 1.0 | 1.0 | 1.0 | 1.0 |
146
- | 0.0068 | 17.1942 | 88000 | 0.1612 | 1.0 | 1.0 | 1.0 | 1.0 |
147
- | 0.0074 | 17.3896 | 89000 | 0.1596 | 1.0 | 1.0 | 1.0 | 1.0 |
148
- | 0.0117 | 17.5850 | 90000 | 0.1617 | 1.0 | 1.0 | 1.0 | 1.0 |
149
- | 0.0116 | 17.7804 | 91000 | 0.1689 | 1.0 | 1.0 | 1.0 | 1.0 |
150
- | 0.0064 | 17.9758 | 92000 | 0.1602 | 1.0 | 1.0 | 1.0 | 1.0 |
151
- | 0.0079 | 18.1712 | 93000 | 0.1647 | 1.0 | 1.0 | 1.0 | 1.0 |
152
- | 0.0051 | 18.3665 | 94000 | 0.1534 | 1.0 | 1.0 | 1.0 | 1.0 |
153
- | 0.0069 | 18.5619 | 95000 | 0.1570 | 1.0 | 1.0 | 1.0 | 1.0 |
154
- | 0.0062 | 18.7573 | 96000 | 0.1533 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0048 | 18.9527 | 97000 | 0.1566 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0142 | 19.1481 | 98000 | 0.1532 | 1.0 | 1.0 | 1.0 | 1.0 |
157
- | 0.0147 | 19.3435 | 99000 | 0.1501 | 1.0 | 1.0 | 1.0 | 1.0 |
158
- | 0.0085 | 19.5389 | 100000 | 0.1535 | 1.0 | 1.0 | 1.0 | 1.0 |
159
- | 0.005 | 19.7343 | 101000 | 0.1599 | 1.0 | 1.0 | 1.0 | 1.0 |
160
- | 0.0066 | 19.9297 | 102000 | 0.1614 | 1.0 | 1.0 | 1.0 | 1.0 |
161
 
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163
  ### Framework versions
 
21
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
  - Loss: 0.1614
 
 
 
 
24
 
25
  ## Model description
26
 
 
49
 
50
  ### Training results
51
 
52
+ | Training Loss | Epoch | Step | Validation Loss |
53
+ |:-------------:|:-------:|:------:|:---------------:|
54
+ | No log | 0 | 0 | 0.2170 |
55
+ | 0.1238 | 0.1954 | 1000 | 0.1458 |
56
+ | 0.1143 | 0.3908 | 2000 | 0.1076 |
57
+ | 0.1208 | 0.5862 | 3000 | 0.1133 |
58
+ | 0.1031 | 0.7816 | 4000 | 0.1069 |
59
+ | 0.1045 | 0.9769 | 5000 | 0.1047 |
60
+ | 0.0974 | 1.1723 | 6000 | 0.1087 |
61
+ | 0.1337 | 1.3677 | 7000 | 0.1209 |
62
+ | 0.0864 | 1.5631 | 8000 | 0.1151 |
63
+ | 0.1049 | 1.7585 | 9000 | 0.1203 |
64
+ | 0.0915 | 1.9539 | 10000 | 0.1255 |
65
+ | 0.079 | 2.1493 | 11000 | 0.1369 |
66
+ | 0.0709 | 2.3447 | 12000 | 0.1167 |
67
+ | 0.0823 | 2.5401 | 13000 | 0.1257 |
68
+ | 0.0725 | 2.7354 | 14000 | 0.1114 |
69
+ | 0.066 | 2.9308 | 15000 | 0.1232 |
70
+ | 0.0354 | 3.1262 | 16000 | 0.1321 |
71
+ | 0.0486 | 3.3216 | 17000 | 0.1245 |
72
+ | 0.0466 | 3.5170 | 18000 | 0.1329 |
73
+ | 0.0523 | 3.7124 | 19000 | 0.1350 |
74
+ | 0.0614 | 3.9078 | 20000 | 0.1373 |
75
+ | 0.0349 | 4.1032 | 21000 | 0.1461 |
76
+ | 0.0315 | 4.2986 | 22000 | 0.1406 |
77
+ | 0.0337 | 4.4939 | 23000 | 0.1355 |
78
+ | 0.0345 | 4.6893 | 24000 | 0.1300 |
79
+ | 0.0324 | 4.8847 | 25000 | 0.1324 |
80
+ | 0.0247 | 5.0801 | 26000 | 0.1347 |
81
+ | 0.0306 | 5.2755 | 27000 | 0.1474 |
82
+ | 0.0267 | 5.4709 | 28000 | 0.1394 |
83
+ | 0.0204 | 5.6663 | 29000 | 0.1487 |
84
+ | 0.0266 | 5.8617 | 30000 | 0.1454 |
85
+ | 0.0188 | 6.0571 | 31000 | 0.1434 |
86
+ | 0.0192 | 6.2524 | 32000 | 0.1458 |
87
+ | 0.0246 | 6.4478 | 33000 | 0.1455 |
88
+ | 0.0219 | 6.6432 | 34000 | 0.1440 |
89
+ | 0.0231 | 6.8386 | 35000 | 0.1561 |
90
+ | 0.0185 | 7.0340 | 36000 | 0.1504 |
91
+ | 0.0163 | 7.2294 | 37000 | 0.1516 |
92
+ | 0.0186 | 7.4248 | 38000 | 0.1451 |
93
+ | 0.0250 | 7.6202 | 39000 | 0.1423 |
94
+ | 0.0217 | 7.8156 | 40000 | 0.1453 |
95
+ | 0.0226 | 8.0109 | 41000 | 0.1584 |
96
+ | 0.0136 | 8.2063 | 42000 | 0.1589 |
97
+ | 0.0186 | 8.4017 | 43000 | 0.1518 |
98
+ | 0.0215 | 8.5971 | 44000 | 0.1459 |
99
+ | 0.0182 | 8.7925 | 45000 | 0.1437 |
100
+ | 0.0236 | 8.9879 | 46000 | 0.1409 |
101
+ | 0.0204 | 9.1833 | 47000 | 0.1514 |
102
+ | 0.0198 | 9.3787 | 48000 | 0.1617 |
103
+ | 0.0158 | 9.5741 | 49000 | 0.1366 |
104
+ | 0.0256 | 9.7694 | 50000 | 0.1450 |
105
+ | 0.0186 | 9.9648 | 51000 | 0.1525 |
106
+ | 0.0238 | 10.1602 | 52000 | 0.1658 |
107
+ | 0.0189 | 10.3556 | 53000 | 0.1442 |
108
+ | 0.0184 | 10.5510 | 54000 | 0.1495 |
109
+ | 0.0196 | 10.7464 | 55000 | 0.1428 |
110
+ | 0.0224 | 10.9418 | 56000 | 0.1606 |
111
+ | 0.0180 | 11.1372 | 57000 | 0.1436 |
112
+ | 0.1813 | 11.3326 | 58000 | 0.1829 |
113
+ | 0.1888 | 11.5279 | 59000 | 0.1832 |
114
+ | 0.1826 | 11.7233 | 60000 | 0.1828 |
115
+ | 0.0227 | 11.9187 | 61000 | 0.1447 |
116
+ | 0.0162 | 12.1141 | 62000 | 0.1491 |
117
+ | 0.0212 | 12.3095 | 63000 | 0.1386 |
118
+ | 0.0124 | 12.5049 | 64000 | 0.1507 |
119
+ | 0.0162 | 12.7003 | 65000 | 0.1425 |
120
+ | 0.0122 | 12.8957 | 66000 | 0.1417 |
121
+ | 0.0105 | 13.0911 | 67000 | 0.1414 |
122
+ | 0.0114 | 13.2864 | 68000 | 0.1537 |
123
+ | 0.0083 | 13.4818 | 69000 | 0.1548 |
124
+ | 0.0084 | 13.6772 | 70000 | 0.1439 |
125
+ | 0.0113 | 13.8726 | 71000 | 0.1504 |
126
+ | 0.0130 | 14.0680 | 72000 | 0.1480 |
127
+ | 0.0066 | 14.2634 | 73000 | 0.1544 |
128
+ | 0.0119 | 14.4588 | 74000 | 0.1509 |
129
+ | 0.0078 | 14.6542 | 75000 | 0.1546 |
130
+ | 0.0162 | 14.8496 | 76000 | 0.1513 |
131
+ | 0.0094 | 15.0449 | 77000 | 0.1571 |
132
+ | 0.0097 | 15.2403 | 78000 | 0.1646 |
133
+ | 0.0132 | 15.4357 | 79000 | 0.1505 |
134
+ | 0.0127 | 15.6311 | 80000 | 0.1539 |
135
+ | 0.0086 | 15.8265 | 81000 | 0.1572 |
136
+ | 0.0067 | 16.0219 | 82000 | 0.1583 |
137
+ | 0.0070 | 16.2173 | 83000 | 0.1531 |
138
+ | 0.0117 | 16.4127 | 84000 | 0.1485 |
139
+ | 0.0156 | 16.6081 | 85000 | 0.1495 |
140
+ | 0.0089 | 16.8034 | 86000 | 0.1570 |
141
+ | 0.0075 | 16.9988 | 87000 | 0.1540 |
142
+ | 0.0068 | 17.1942 | 88000 | 0.1612 |
143
+ | 0.0074 | 17.3896 | 89000 | 0.1596 |
144
+ | 0.0117 | 17.5850 | 90000 | 0.1617 |
145
+ | 0.0116 | 17.7804 | 91000 | 0.1689 |
146
+ | 0.0064 | 17.9758 | 92000 | 0.1602 |
147
+ | 0.0079 | 18.1712 | 93000 | 0.1647 |
148
+ | 0.0051 | 18.3665 | 94000 | 0.1534 |
149
+ | 0.0069 | 18.5619 | 95000 | 0.1570 |
150
+ | 0.0062 | 18.7573 | 96000 | 0.1533 |
151
+ | 0.0048 | 18.9527 | 97000 | 0.1566 |
152
+ | 0.0142 | 19.1481 | 98000 | 0.1532 |
153
+ | 0.0147 | 19.3435 | 99000 | 0.1501 |
154
+ | 0.0085 | 19.5389 | 100000 | 0.1535 |
155
+ | 0.0050 | 19.7343 | 101000 | 0.1599 |
156
+ | 0.0066 | 19.9297 | 102000 | 0.1614 |
157
 
158
 
159
  ### Framework versions