entity_to_acronyms = {
'Activity': 'ACT',
'Administration': 'ADM',
'Age': 'AGE',
'Area': 'ARA',
'Biological_attribute': 'BAT',
'Biological_structure': 'BST',
'Clinical_event': 'CLE',
'Color': 'COL',
'Coreference': 'COR',
'Date': 'DAT',
'Detailed_description': 'DET',
'Diagnostic_procedure': 'DIA',
'Disease_disorder': 'DIS',
'Distance': 'DIS',
'Dosage': 'DOS',
'Duration': 'DUR',
'Family_history': 'FAM',
'Frequency': 'FRE',
'Height': 'HEI',
'History': 'HIS',
'Lab_value': 'LAB',
'Mass': 'MAS',
'Medication': 'MED',
'Nonbiological_location': 'NBL',
'Occupation': 'OCC',
'Other_entity': 'OTH',
'Other_event': 'OTE',
'Outcome': 'OUT',
'Personal_background': 'PER',
'Qualitative_concept': 'QUC',
'Quantitative_concept': 'QUC',
'Severity': 'SEV',
'Sex': 'SEX',
'Shape': 'SHA',
'Sign_symptom': 'SIG',
'Subject': 'SUB',
'Texture': 'TEX',
'Therapeutic_procedure': 'THP',
'Time': 'TIM',
'Volume': 'VOL',
'Weight': 'WEI'
}
index_to_label = {1: 'B-ACT',
2: 'B-ADM',
3: 'B-AGE',
4: 'B-ARA',
5: 'B-BAT',
6: 'B-BST',
7: 'B-CLE',
8: 'B-COL',
9: 'B-COR',
10: 'B-DAT',
11: 'B-DET',
12: 'B-DIA',
13: 'B-DIS',
14: 'B-DOS',
15: 'B-DUR',
16: 'B-FAM',
17: 'B-FRE',
18: 'B-HEI',
19: 'B-HIS',
20: 'B-LAB',
21: 'B-MAS',
22: 'B-MED',
23: 'B-NBL',
24: 'B-OCC',
25: 'B-OTE',
26: 'B-OTH',
27: 'B-OUT',
28: 'B-PER',
29: 'B-QUC',
30: 'B-SEV',
31: 'B-SEX',
32: 'B-SHA',
33: 'B-SIG',
34: 'B-SUB',
35: 'B-TEX',
36: 'B-THP',
37: 'B-TIM',
38: 'B-VOL',
39: 'B-WEI',
40: 'I-ACT',
41: 'I-ADM',
42: 'I-AGE',
43: 'I-ARA',
44: 'I-BAT',
45: 'I-BST',
46: 'I-CLE',
47: 'I-COL',
48: 'I-COR',
49: 'I-DAT',
50: 'I-DET',
51: 'I-DIA',
52: 'I-DIS',
53: 'I-DOS',
54: 'I-DUR',
55: 'I-FAM',
56: 'I-FRE',
57: 'I-HEI',
58: 'I-HIS',
59: 'I-LAB',
60: 'I-MAS',
61: 'I-MED',
62: 'I-NBL',
63: 'I-OCC',
64: 'I-OTE',
65: 'I-OTH',
66: 'I-OUT',
67: 'I-PER',
68: 'I-QUC',
69: 'I-SEV',
70: 'I-SHA',
71: 'I-SIG',
72: 'I-SUB',
73: 'I-TEX',
74: 'I-THP',
75: 'I-TIM',
76: 'I-VOL',
77: 'I-WEI',
78: 'O',
0: '<PAD>'}
MAX_LENGTH = 100
acronyms_to_entities = {v: k for k, v in entity_to_acronyms.items()}
models = {
"model_1": {
"path": "../models/model_1.h5",
"title": "Bidirectional LSTM Model with single LSTM layer"
},
"model_2": {
"path": "../models/model_2.h5",
"title": "Bidirectional LSTM Model with two LSTM layers and one Hidden Dense Layer"
},
"model_3": {
"path": "../models/model_3.h5",
"title": "Bidirectional LSTM model with BioWordVecEmbedding Layers followed two LSTM layers and one Hidden Dense Layer"
},
"model_4": {
"path": "../models/model_4.h5",
"title": "Bidirectional LSTM Model with Time Distributed Dense Layers - Single LSTM layer and Two time distributed dense layers"
},
"model_5": {
"path": "../models/model_5.h5",
"title": "Bidirectional LSTM Model with two LSTM layers and one Hidden Dense Layer"
},
"model_6": {
"path": "../models/model_6.h5",
"title": "Bidirectional LSTM Time Distirbuted Dense Layers and Convolutional 1D layer"
},
"model_7": {
"path": "../models/model_7.h5",
"title": "Bidirectional LSTM CRF model with BioWordVecEmbedding Layers followed two LSTM layers, Time Distirbuted Dense Layers and Convolutional 1D layer"
}
}
