Text Classification
Transformers
Safetensors
English
bibr
OECD
scientific-paper-classification
MiniLM
Instructions to use scienceverse/bibr-paper-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scienceverse/bibr-paper-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scienceverse/bibr-paper-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scienceverse/bibr-paper-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,349 Bytes
c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e b4ef7f4 c4dd45e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | {
"classification_report": {
"Agricultural and Veterinary Sciences": {
"f1-score": 0.7455357142857143,
"precision": 0.7636031092821216,
"recall": 0.7283035324901875,
"support": 2293.0
},
"Engineering and Technology": {
"f1-score": 0.804904214559387,
"precision": 0.8033037626185378,
"recall": 0.8065110565110565,
"support": 3256.0
},
"Humanities and the Arts": {
"f1-score": 0.7972758229284903,
"precision": 0.797819173103135,
"recall": 0.7967332123411979,
"support": 2204.0
},
"Medical and Health Sciences": {
"f1-score": 0.8291206296352354,
"precision": 0.8154212563262876,
"recall": 0.8432881773399015,
"support": 3248.0
},
"Natural Sciences": {
"f1-score": 0.6668681069647983,
"precision": 0.6720462850182704,
"recall": 0.6617691154422789,
"support": 3335.0
},
"Social Sciences": {
"f1-score": 0.7857319791924696,
"precision": 0.7822441430332923,
"recall": 0.7892510574769843,
"support": 4019.0
},
"accuracy": 0.7719967311359303,
"macro avg": {
"f1-score": 0.7715727445943492,
"precision": 0.7724062882302741,
"recall": 0.7709760252669344,
"support": 18355.0
},
"weighted avg": {
"f1-score": 0.7715784906213174,
"precision": 0.7713698855964962,
"recall": 0.7719967311359303,
"support": 18355.0
}
},
"confidence_curve": [
{
"accuracy": 0.7719967311359303,
"coverage": 1.0,
"macro_f1": 0.7715727445943492,
"rows": 18355.0,
"threshold": 0.0
},
{
"accuracy": 0.7976120456416892,
"coverage": 0.9262871152274584,
"macro_f1": 0.7967970019133345,
"rows": 17002.0,
"threshold": 0.5
},
{
"accuracy": 0.8593491035106222,
"coverage": 0.7231816943612095,
"macro_f1": 0.8561099696894608,
"rows": 13274.0,
"threshold": 0.7
},
{
"accuracy": 0.8935934177683277,
"coverage": 0.5893217107055299,
"macro_f1": 0.8880868103664564,
"rows": 10817.0,
"threshold": 0.8
},
{
"accuracy": 0.9318342525809645,
"coverage": 0.38523563061836014,
"macro_f1": 0.9213157044536953,
"rows": 7071.0,
"threshold": 0.9
},
{
"accuracy": 0.961308750973773,
"coverage": 0.2098065922092073,
"macro_f1": 0.9281730445872894,
"rows": 3851.0,
"threshold": 0.95
}
],
"confusion_matrix": {
"labels": [
"Agricultural and Veterinary Sciences",
"Engineering and Technology",
"Humanities and the Arts",
"Medical and Health Sciences",
"Natural Sciences",
"Social Sciences"
],
"matrix": [
[
1670,
53,
12,
143,
307,
108
],
[
80,
2626,
12,
61,
351,
126
],
[
19,
21,
1756,
46,
47,
315
],
[
98,
54,
30,
2739,
201,
126
],
[
225,
391,
55,
249,
2207,
208
],
[
95,
124,
336,
121,
171,
3172
]
]
}
}
|