Instructions to use ai4data/gliner2_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use ai4data/gliner2_datause with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("ai4data/gliner2_datause") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Upload holdout_metrics.json with huggingface_hub
Browse files- holdout_metrics.json +118 -0
holdout_metrics.json
ADDED
|
@@ -0,0 +1,118 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"labels": [
|
| 3 |
+
"NAMED_DATA",
|
| 4 |
+
"DESCRIPTIVE_DATA",
|
| 5 |
+
"VAGUE_DATA"
|
| 6 |
+
],
|
| 7 |
+
"in_domain": {
|
| 8 |
+
"rows": [
|
| 9 |
+
{
|
| 10 |
+
"thr": 0.1,
|
| 11 |
+
"tp": 12163,
|
| 12 |
+
"fp": 5439,
|
| 13 |
+
"fn": 404,
|
| 14 |
+
"precision": 0.6910010226110669,
|
| 15 |
+
"recall": 0.9678523116097716,
|
| 16 |
+
"f05": 0.7329316059053931,
|
| 17 |
+
"f1": 0.8063243727004541
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"thr": 0.2,
|
| 21 |
+
"tp": 11976,
|
| 22 |
+
"fp": 4277,
|
| 23 |
+
"fn": 591,
|
| 24 |
+
"precision": 0.736848581800283,
|
| 25 |
+
"recall": 0.9529720697063738,
|
| 26 |
+
"f05": 0.7718583637324533,
|
| 27 |
+
"f1": 0.8310895211658571
|
| 28 |
+
},
|
| 29 |
+
{
|
| 30 |
+
"thr": 0.3,
|
| 31 |
+
"tp": 11788,
|
| 32 |
+
"fp": 3535,
|
| 33 |
+
"fn": 779,
|
| 34 |
+
"precision": 0.7693010507080859,
|
| 35 |
+
"recall": 0.9380122543168616,
|
| 36 |
+
"f05": 0.7980070133633002,
|
| 37 |
+
"f1": 0.8453209035496594
|
| 38 |
+
},
|
| 39 |
+
{
|
| 40 |
+
"thr": 0.4,
|
| 41 |
+
"tp": 11618,
|
| 42 |
+
"fp": 2942,
|
| 43 |
+
"fn": 949,
|
| 44 |
+
"precision": 0.7979395604395605,
|
| 45 |
+
"recall": 0.9244847616774091,
|
| 46 |
+
"f05": 0.8203991130820399,
|
| 47 |
+
"f1": 0.856563571349578
|
| 48 |
+
},
|
| 49 |
+
{
|
| 50 |
+
"thr": 0.5,
|
| 51 |
+
"tp": 11383,
|
| 52 |
+
"fp": 2478,
|
| 53 |
+
"fn": 1184,
|
| 54 |
+
"precision": 0.8212250198398384,
|
| 55 |
+
"recall": 0.9057849924405188,
|
| 56 |
+
"f05": 0.8368499213362546,
|
| 57 |
+
"f1": 0.8614348418344181
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"thr": 0.6,
|
| 61 |
+
"tp": 11068,
|
| 62 |
+
"fp": 2008,
|
| 63 |
+
"fn": 1499,
|
| 64 |
+
"precision": 0.8464362190272254,
|
| 65 |
+
"recall": 0.8807193443144744,
|
| 66 |
+
"f05": 0.8530776464059442,
|
| 67 |
+
"f1": 0.8632375307101354
|
| 68 |
+
},
|
| 69 |
+
{
|
| 70 |
+
"thr": 0.7,
|
| 71 |
+
"tp": 10487,
|
| 72 |
+
"fp": 1535,
|
| 73 |
+
"fn": 2080,
|
| 74 |
+
"precision": 0.8723174180668773,
|
| 75 |
+
"recall": 0.8344871488819925,
|
| 76 |
+
"f05": 0.8644794328579674,
|
| 77 |
+
"f1": 0.8529830411972833
|
| 78 |
+
}
|
| 79 |
+
],
|
| 80 |
+
"best_f05": {
|
| 81 |
+
"thr": 0.7,
|
| 82 |
+
"tp": 10487,
|
| 83 |
+
"fp": 1535,
|
| 84 |
+
"fn": 2080,
|
| 85 |
+
"precision": 0.8723174180668773,
|
| 86 |
+
"recall": 0.8344871488819925,
|
| 87 |
+
"f05": 0.8644794328579674,
|
| 88 |
+
"f1": 0.8529830411972833
|
| 89 |
+
},
|
| 90 |
+
"best_f1": {
|
| 91 |
+
"thr": 0.6,
|
| 92 |
+
"tp": 11068,
|
| 93 |
+
"fp": 2008,
|
| 94 |
+
"fn": 1499,
|
| 95 |
+
"precision": 0.8464362190272254,
|
| 96 |
+
"recall": 0.8807193443144744,
|
| 97 |
+
"f05": 0.8530776464059442,
|
| 98 |
+
"f1": 0.8632375307101354
|
| 99 |
+
},
|
| 100 |
+
"per_label": {
|
| 101 |
+
"named_data": [
|
| 102 |
+
4494,
|
| 103 |
+
977,
|
| 104 |
+
1089
|
| 105 |
+
],
|
| 106 |
+
"descriptive_data": [
|
| 107 |
+
4316,
|
| 108 |
+
1341,
|
| 109 |
+
1576
|
| 110 |
+
],
|
| 111 |
+
"vague_data": [
|
| 112 |
+
566,
|
| 113 |
+
424,
|
| 114 |
+
535
|
| 115 |
+
]
|
| 116 |
+
}
|
| 117 |
+
}
|
| 118 |
+
}
|