reduce the size of the LSTM to try avoiding OOM

kermitt2 · Jul 13, 2023 · 536e0de · 536e0de
1 parent 09af3d1
commit 536e0de
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/delft/sequenceLabelling/models.py b/delft/sequenceLabelling/models.py
@@ -1205,7 +1205,7 @@ def __init__(self, config, ntags=None, load_pretrained_weights: bool = True, loc
         embedding_layer = transformer_layers(input_ids_in, token_type_ids=token_type_ids, attention_mask=attention_mask)[0]
         embedding_layer = Dropout(0.1)(embedding_layer)
 
-        bid_lstm = Bidirectional(LSTM(units=embedding_layer.shape[-1],
+        bid_lstm = Bidirectional(LSTM(units=config.num_word_lstm_units, #embedding_layer.shape[-1],
                                return_sequences=True,
                                recurrent_dropout=config.recurrent_dropout))(embedding_layer)
         bid_lstm = Dropout(config.dropout)(bid_lstm)
@@ -1239,7 +1239,7 @@ def __init__(self, config, ntags=None, load_pretrained_weights: bool = True, loc
         embedding_layer = transformer_layers(input_ids_in, token_type_ids=token_type_ids, attention_mask=attention_mask)[0]
         embedding_layer = Dropout(0.1)(embedding_layer)
 
-        bid_lstm = Bidirectional(LSTM(units=embedding_layer.shape[-1],
+        bid_lstm = Bidirectional(LSTM(units=config.num_word_lstm_units, #embedding_layer.shape[-1],
                                       return_sequences=True,
                                       recurrent_dropout=config.recurrent_dropout))(embedding_layer)
         bid_lstm = Dropout(config.dropout)(bid_lstm)
@@ -1276,7 +1276,7 @@ def __init__(self, config, ntags=None, load_pretrained_weights: bool = True, loc
         embedding_layer = transformer_layers(input_ids_in, token_type_ids=token_type_ids, attention_mask=attention_mask)[0]
         embedding_layer = Dropout(0.1)(embedding_layer)
 
-        bid_lstm = Bidirectional(LSTM(units=embedding_layer.shape[-1],
+        bid_lstm = Bidirectional(LSTM(units=config.num_word_lstm_units, #embedding_layer.shape[-1],
                                       return_sequences=True,
                                       recurrent_dropout=config.recurrent_dropout))(embedding_layer)
         bid_lstm = Dropout(config.dropout)(bid_lstm)