config.py

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"
    }
}