Updates train_bidaf.py

docqa/scripts/train_bidaf.py

@@ -1,12 +1,10 @@
-from docqa.data_processing.paragraph_qa import ContextLenKey, ContextLenBucketedKey, DocumentQaTrainingData
-from docqa.squad.squad_eval import SentenceSpanEvaluator, SquadSpanEvaluator, BoundedSquadSpanEvaluator
-
 from docqa import model_dir
 from docqa import trainer
+from docqa.data_processing.qa_training_data import ContextLenBucketedKey, ContextLenKey
 from docqa.dataset import ClusteredBatcher
 from docqa.doc_qa_models import Attention
 from docqa.encoder import DocumentAndQuestionEncoder, SingleSpanAnswerEncoder
-from docqa.evaluator import LossEvaluator
+from docqa.evaluator import LossEvaluator, SpanEvaluator
 from docqa.nn.attention import BiAttention
 from docqa.nn.embedder import FixedWordEmbedder, CharWordEmbedder, LearnedCharEmbedder
 from docqa.nn.layers import NullBiMapper, NullMapper, SequenceMapperSeq, ReduceLayer, Conv1d, HighwayLayer, ChainConcat, \
@@ -15,6 +13,7 @@
 from docqa.nn.similarity_layers import TriLinear
 from docqa.nn.span_prediction import BoundsPredictor
 from docqa.squad.build_squad_dataset import SquadCorpus
+from docqa.squad.squad_data import DocumentQaTrainingData
 from docqa.trainer import SerializableOptimizer, TrainParams
 from docqa.utils import get_output_name_from_cli
 
@@ -52,6 +51,7 @@ def main():
         embed_mapper=SequenceMapperSeq(
             HighwayLayer(activation="relu"), HighwayLayer(activation="relu"),
             recurrent_layer),
+        preprocess=None,
         question_mapper=None,
         context_mapper=None,
         memory_builder=NullBiMapper(),
@@ -64,20 +64,21 @@ def main():
                     recurrent_layer),
                 end_layer=recurrent_layer
             )
-        )
+        ),
+
     )
 
     with open(__file__, "r") as f:
         notes = f.read()
 
-    eval = [LossEvaluator(), SquadSpanEvaluator(), BoundedSquadSpanEvaluator([18]), SentenceSpanEvaluator()]
+    eval = [LossEvaluator(), SpanEvaluator(bound=[17], text_eval="squad")]
 
     corpus = SquadCorpus()
     train_batching = ClusteredBatcher(60, ContextLenBucketedKey(3), True, False)
     eval_batching = ClusteredBatcher(60, ContextLenKey(), False, False)
     data = DocumentQaTrainingData(corpus, None, train_batching, eval_batching)
 
-    trainer.start_training(data, model, train_params, eval, model_dir.ModelDir(out), notes, False)
+    trainer.start_training(data, model, train_params, eval, model_dir.ModelDir(out), notes)
 
 
 if __name__ == "__main__":