GLiNER
PyTorch
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Update README.md

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@@ -49,53 +49,100 @@ The following hyperparameters were used during training:
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  | Epoch | Training Loss |
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  |:-----:|:-------------:|
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  | 1 | No log |
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- | 2 | No log |
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- | 3 | 75.730700 |
54
- | 4 | 75.730700 |
55
- | 5 | 75.730700 |
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- | 6 | 61.001500 |
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- | 7 | 61.001500 |
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- | 8 | 54.493900 |
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- | 9 | 54.493900 |
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- | 10 | 54.493900 |
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- | 11 | 48.829000 |
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- | 12 | 48.829000 |
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- | 13 | 44.892000 |
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- | 14 | 44.892000 |
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- | 15 | 44.892000 |
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- | 16 | 41.297600 |
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- | 17 | 41.297600 |
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- | 18 | 41.297600 |
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- | 19 | 37.768500 |
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- | 20 | 37.768500 |
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- | 21 | 35.017400 |
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- | 22 | 35.017400 |
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- | 23 | 35.017400 |
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- | 24 | 32.340500 |
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- | 25 | 32.340500 |
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- | 26 | 29.995400 |
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- | 27 | 29.995400 |
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- | 28 | 29.995400 |
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- | 29 | 28.467700 |
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- | 30 | 28.467700 |
81
- | 31 | 28.467700 |
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- | 32 | 26.469200 |
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- | 33 | 26.469200 |
84
- | 34 | 25.156200 |
85
- | 35 | 25.156200 |
86
- | 36 | 25.156200 |
87
- | 37 | 24.252900 |
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- | 38 | 24.252900 |
89
- | 39 | 23.941300 |
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- | 40 | 23.941300 |
91
- | 41 | 23.941300 |
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- | 42 | 22.776800 |
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- | 43 | 22.776800 |
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- | 44 | 22.776800 |
95
- | 45 | 23.013400 |
96
- | 46 | 23.013400 |
97
- | 47 | 22.030100 |
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- | 48 | 22.030100 |
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- | 49 | 22.030100 |
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- | 50 | 21.937700 |
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- | 51 | 21.948900 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
49
  | Epoch | Training Loss |
50
  |:-----:|:-------------:|
51
  | 1 | No log |
52
+ | 2 | 2008.786600 |
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+ | 3 | 2008.786600 |
54
+ | 4 | 117.661100 |
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+ | 5 | 84.863400 |
56
+ | 6 | 84.863400 |
57
+ | 7 | 66.872200 |
58
+ | 8 | 66.872200 |
59
+ | 9 | 58.574600 |
60
+ | 10 | 53.905900 |
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+ | 11 | 53.905900 |
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+ | 12 | 48.563900 |
63
+ | 13 | 48.563900 |
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+ | 14 | 43.970700 |
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+ | 15 | 38.940100 |
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+ | 16 | 38.940100 |
67
+ | 17 | 35.543100 |
68
+ | 18 | 35.543100 |
69
+ | 19 | 33.050500 |
70
+ | 20 | 30.091100 |
71
+ | 21 | 30.091100 |
72
+ | 22 | 27.275200 |
73
+ | 23 | 27.275200 |
74
+ | 24 | 25.327500 |
75
+ | 25 | 23.171200 |
76
+ | 26 | 23.171200 |
77
+ | 27 | 20.940300 |
78
+ | 28 | 19.034100 |
79
+ | 29 | 19.034100 |
80
+ | 30 | 17.366400 |
81
+ | 31 | 17.366400 |
82
+ | 32 | 16.570800 |
83
+ | 33 | 15.673200 |
84
+ | 34 | 15.673200 |
85
+ | 35 | 14.457500 |
86
+ | 36 | 14.457500 |
87
+ | 37 | 13.064500 |
88
+ | 38 | 12.786100 |
89
+ | 39 | 12.786100 |
90
+ | 40 | 11.934400 |
91
+ | 41 | 11.934400 |
92
+ | 42 | 11.225800 |
93
+ | 43 | 10.106500 |
94
+ | 44 | 10.106500 |
95
+ | 45 | 9.200000 |
96
+ | 46 | 9.200000 |
97
+ | 47 | 9.449100 |
98
+ | 48 | 8.979400 |
99
+ | 49 | 8.979400 |
100
+ | 50 | 7.840100 |
101
+ | 51 | 7.949600 |
102
+ | 52 | 7.949600 |
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+ | 53 | 7.233800 |
104
+ | 54 | 7.233800 |
105
+ | 55 | 7.383200 |
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+ | 56 | 6.114800 |
107
+ | 57 | 6.114800 |
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+ | 58 | 6.421800 |
109
+ | 59 | 6.421800 |
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+ | 60 | 6.191000 |
111
+ | 61 | 5.932200 |
112
+ | 62 | 5.932200 |
113
+ | 63 | 5.706100 |
114
+ | 64 | 5.706100 |
115
+ | 65 | 5.567800 |
116
+ | 66 | 5.104100 |
117
+ | 67 | 5.104100 |
118
+ | 68 | 5.407800 |
119
+ | 69 | 5.407800 |
120
+ | 70 | 5.607500 |
121
+ | 71 | 4.967500 |
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+ | 72 | 4.967500 |
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+ | 73 | 5.362100 |
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+ | 74 | 5.362100 |
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+ | 75 | 5.425800 |
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+ | 76 | 5.283100 |
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+ | 77 | 5.283100 |
128
+ | 78 | 4.250000 |
129
+ | 79 | 4.330900 |
130
+ | 80 | 4.330900 |
131
+ | 81 | 4.088400 |
132
+ | 82 | 4.088400 |
133
+ | 83 | 4.512400 |
134
+ | 84 | 4.513500 |
135
+ | 85 | 4.513500 |
136
+ | 86 | 4.327000 |
137
+ | 87 | 4.327000 |
138
+ | 88 | 5.152200 |
139
+ | 89 | 3.776100 |
140
+ | 90 | 3.776100 |
141
+ | 91 | 3.762500 |
142
+ | 92 | 3.762500 |
143
+ | 93 | 4.054900 |
144
+ | 94 | 3.579700 |
145
+ | 95 | 3.579700 |
146
+ | 96 | 3.391500 |
147
+ | 97 | 3.391500 |
148
+ | 98 | 4.863200 |