Relation extraction

medcat/config_re.py

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+from typing import Dict, Any
+from medcat.config import ConfigMixin
+
+
+class ConfigRE(ConfigMixin):
+
+    def __init__(self) -> None:
+
+        self.general: Dict[str, Any] = {
+                'device': 'cpu',
+                'seed': 13,
+                 # these are the pairs of relations that are to be predicted , form of : [("Disease", "Symptom"), ("entity1_type", "entity2_type") ...]
+                'relation_type_filter_pairs': [],
+                'category_value2id': {}, # Map from category values to ID, if empty it will be autocalculated during training
+                'vocab_size': None, # Will be set automatically if the tokenizer is provided during meta_cat init
+                'lowercase': True, # If true all input text will be lowercased
+                'ent_context_left': 2, # Number of entities to take from the left of the concept
+                'ent_context_right': 2, # Number of entities to take from the right of the concept
+                'window_size' : 300, # max acceptable dinstance between entities (in characters)
+                'batch_size_eval': 5000, # Number of annotations to be meta-annotated at once in eval
+                'tokenizer_name': 'BERT', # Tokenizer name used with of MetaCAT
+                'pipe_batch_size_in_chars': 20000000, # How many characters are piped at once into the meta_cat class
+                }
+        self.model: Dict[str, Any] = {
+                'model_name': 'BERT',
+                'input_size': 300,
+                'hidden_size': 300,
+                'dropout': 0.5,
+                'nclasses': 2, # Number of classes that this model will output
+                'padding_idx': -1,
+                'emb_grad': True, # If True the embeddings will also be trained
+                }
+
+        self.train: Dict[str, Any] = {
+                'batch_size': 100,
+                'nepochs': 50,
+                'lr': 0.001,
+                'test_size': 0.1,
+                'shuffle_data': True, # Used only during training, if set the dataset will be shuffled before train/test split
+                'class_weights': None,
+                'score_average': 'weighted', # What to use for averaging F1/P/R across labels
+                'prerequisites': {},
+                'auto_save_model': True, # Should do model be saved during training for best results
+                }