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MarinaTA
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FeverCodeChallenge

Text Classification
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deberta-v2
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  # Evaluation results
80
  | TEST | precision | recall | f1-score | support |
81
  |--------------|-----------|--------|----------|---------|
82
- | 0 | 0.56 | 0.36 | 0.44 | 5327 |
83
- | 1 | 0.80 | 0.84 | 0.82 | 1595 |
84
- | 2 | 0.79 | 0.70 | 0.74 | 2224 |
85
- | 3 | 0.79 | 0.79 | 0.79 | 1190 |
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- | 4 | 0.88 | 0.92 | 0.90 | 2632 |
87
- | 5 | 0.98 | 0.97 | 0.98 | 4775 |
88
- | 6 | 0.72 | 0.77 | 0.74 | 1024 |
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- | 7 | 0.61 | 0.68 | 0.64 | 1111 |
90
- | 8 | 0.77 | 0.81 | 0.79 | 9765 |
91
- | 9 | 0.94 | 0.94 | 0.94 | 840 |
92
- | 10 | 0.93 | 0.99 | 0.96 | 1639 |
93
- | 11 | 0.61 | 0.47 | 0.53 | 539 |
94
- | 12 | 0.58 | 0.60 | 0.59 | 3802 |
95
- | 13 | 0.74 | 0.82 | 0.78 | 2476 |
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- | 14 | 0.84 | 0.93 | 0.88 | 813 |
97
- | 15 | 0.84 | 0.87 | 0.86 | 3004 |
98
- | 16 | 0.68 | 0.63 | 0.66 | 2031 |
99
- | 17 | 0.89 | 0.88 | 0.89 | 577 |
100
- | 18 | 0.70 | 0.69 | 0.69 | 1813 |
101
- | 19 | 0.70 | 0.85 | 0.77 | 3840 |
102
- | 20 | 0.88 | 0.91 | 0.89 | 3253 |
103
- | 21 | 0.73 | 0.78 | 0.76 | 446 |
104
- | accuracy | | | 0.77 | 54716 |
105
- | macro avg | 0.77 | 0.78 | 0.77 | 54716 |
106
- | weighted avg | 0.76 | 0.77 | 0.76 | 54716 |
107
 
108
  | VALIDATION | precision | recall | f1-score | support |
109
  |--------------|-----------|--------|----------|---------|
110
- | 0 | 0.52 | 0.23 | 0.32 | 1034 |
111
- | 1 | 0.79 | 0.84 | 0.82 | 1747 |
112
- | 2 | 0.79 | 0.70 | 0.74 | 2273 |
113
- | 3 | 0.85 | 0.85 | 0.85 | 2982 |
114
- | 4 | 0.89 | 0.92 | 0.91 | 2236 |
115
  | 5 | 0.97 | 0.98 | 0.97 | 2893 |
116
- | 6 | 0.86 | 0.73 | 0.79 | 1335 |
117
- | 7 | 0.70 | 0.76 | 0.73 | 837 |
118
- | 8 | 0.74 | 0.71 | 0.73 | 790 |
119
- | 9 | 0.92 | 0.97 | 0.95 | 839 |
120
- | 10 | 0.96 | 0.99 | 0.97 | 13182 |
121
- | 11 | 0.47 | 0.25 | 0.33 | 907 |
122
- | 12 | 0.56 | 0.64 | 0.59 | 965 |
123
- | 13 | 0.82 | 0.84 | 0.83 | 2780 |
124
- | 14 | 0.94 | 0.92 | 0.93 | 1245 |
125
- | 15 | 0.85 | 0.91 | 0.88 | 930 |
126
- | 16 | 0.82 | 0.87 | 0.85 | 3226 |
127
- | 17 | 0.95 | 0.96 | 0.96 | 2633 |
128
- | 18 | 0.73 | 0.69 | 0.71 | 2518 |
129
- | 19 | 0.67 | 0.80 | 0.73 | 2303 |
130
- | 20 | 0.91 | 0.90 | 0.91 | 6032 |
131
- | 21 | 0.70 | 0.90 | 0.79 | 203 |
132
- | accuracy | | | 0.86 | 53890 |
133
- | macro avg | 0.79 | 0.79 | 0.78 | 53890 |
134
- | weighted avg | 0.86 | 0.86 | 0.86 | 53890 |
135
 
136
  # Training results
137
  | train_runtime | train_samples_per_second | train_steps_per_second | train_loss | epoch |
 
79
  # Evaluation results
80
  | TEST | precision | recall | f1-score | support |
81
  |--------------|-----------|--------|----------|---------|
82
+ | 0 | 0.56 | 0.42 | 0.48 | 5327 |
83
+ | 1 | 0.81 | 0.86 | 0.83 | 1595 |
84
+ | 2 | 0.75 | 0.76 | 0.76 | 2224 |
85
+ | 3 | 0.80 | 0.82 | 0.81 | 1190 |
86
+ | 4 | 0.93 | 0.92 | 0.93 | 2632 |
87
+ | 5 | 0.99 | 0.97 | 0.98 | 4775 |
88
+ | 6 | 0.74 | 0.80 | 0.77 | 1024 |
89
+ | 7 | 0.71 | 0.64 | 0.67 | 1111 |
90
+ | 8 | 0.79 | 0.80 | 0.80 | 9765 |
91
+ | 9 | 0.94 | 0.93 | 0.94 | 840 |
92
+ | 10 | 0.94 | 0.98 | 0.96 | 1639 |
93
+ | 11 | 0.62 | 0.52 | 0.56 | 539 |
94
+ | 12 | 0.57 | 0.74 | 0.64 | 3802 |
95
+ | 13 | 0.79 | 0.84 | 0.81 | 2476 |
96
+ | 14 | 0.83 | 0.94 | 0.88 | 813 |
97
+ | 15 | 0.88 | 0.87 | 0.87 | 3004 |
98
+ | 16 | 0.76 | 0.61 | 0.68 | 2031 |
99
+ | 17 | 0.88 | 0.88 | 0.88 | 577 |
100
+ | 18 | 0.73 | 0.71 | 0.72 | 1813 |
101
+ | 19 | 0.79 | 0.85 | 0.82 | 3840 |
102
+ | 20 | 0.89 | 0.91 | 0.90 | 3253 |
103
+ | 21 | 0.69 | 0.75 | 0.72 | 446 |
104
+ | accuracy | | | 0.79 | 54716 |
105
+ | macro avg | 0.79 | 0.80 | 0.79 | 54716 |
106
+ | weighted avg | 0.79 | 0.79 | 0.79 | 54716 |
107
 
108
  | VALIDATION | precision | recall | f1-score | support |
109
  |--------------|-----------|--------|----------|---------|
110
+ | 0 | 0.55 | 0.32 | 0.40 | 1034 |
111
+ | 1 | 0.79 | 0.85 | 0.82 | 1747 |
112
+ | 2 | 0.75 | 0.78 | 0.76 | 2273 |
113
+ | 3 | 0.84 | 0.88 | 0.86 | 2982 |
114
+ | 4 | 0.93 | 0.92 | 0.93 | 2236 |
115
  | 5 | 0.97 | 0.98 | 0.97 | 2893 |
116
+ | 6 | 0.88 | 0.76 | 0.81 | 1335 |
117
+ | 7 | 0.77 | 0.74 | 0.75 | 837 |
118
+ | 8 | 0.76 | 0.73 | 0.74 | 790 |
119
+ | 9 | 0.95 | 0.96 | 0.95 | 839 |
120
+ | 10 | 0.96 | 0.98 | 0.97 | 13182 |
121
+ | 11 | 0.50 | 0.30 | 0.37 | 907 |
122
+ | 12 | 0.55 | 0.74 | 0.64 | 965 |
123
+ | 13 | 0.83 | 0.86 | 0.85 | 2780 |
124
+ | 14 | 0.93 | 0.94 | 0.93 | 1245 |
125
+ | 15 | 0.89 | 0.92 | 0.91 | 930 |
126
+ | 16 | 0.87 | 0.85 | 0.86 | 3226 |
127
+ | 17 | 0.96 | 0.97 | 0.96 | 2633 |
128
+ | 18 | 0.75 | 0.71 | 0.73 | 2518 |
129
+ | 19 | 0.74 | 0.81 | 0.77 | 2303 |
130
+ | 20 | 0.92 | 0.91 | 0.92 | 6032 |
131
+ | 21 | 0.72 | 0.89 | 0.79 | 203 |
132
+ | accuracy | | | 0.87 | 53890 |
133
+ | macro avg | 0.81 | 0.81 | 0.81 | 53890 |
134
+ | weighted avg | 0.87 | 0.87 | 0.87 | 53890 |
135
 
136
  # Training results
137
  | train_runtime | train_samples_per_second | train_steps_per_second | train_loss | epoch |