From 0f5b61d20aeff527f4e03d5b38353b2a753b11cb Mon Sep 17 00:00:00 2001 From: timoeller Date: Mon, 24 Feb 2020 12:28:49 +0100 Subject: [PATCH] Fix typo --- haystack/__init__.py | 2 +- haystack/reader/farm.py | 10 ++++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/haystack/__init__.py b/haystack/__init__.py index 916fe5259..42dc06718 100644 --- a/haystack/__init__.py +++ b/haystack/__init__.py @@ -46,7 +46,7 @@ class Finder: # 3) Apply reader to get granular answer(s) logger.info(f"Applying the reader now to look for the answer in detail ...") results = self.reader.predict(question=question, - paragrahps=paragraphs, + paragraphs=paragraphs, meta_data_paragraphs=meta_data, top_k=top_k_reader) diff --git a/haystack/reader/farm.py b/haystack/reader/farm.py index 43c31173c..cf0325742 100644 --- a/haystack/reader/farm.py +++ b/haystack/reader/farm.py @@ -145,7 +145,7 @@ class FARMReader: self.inferencer.model.save(directory) self.inferencer.processor.save(directory) - def predict(self, question, paragrahps, meta_data_paragraphs=None, top_k=None, max_processes=1): + def predict(self, question, paragraphs, meta_data_paragraphs=None, top_k=None, max_processes=1): """ Use loaded QA model to find answers for a question in the supplied paragraphs. @@ -175,12 +175,12 @@ class FARMReader: """ if meta_data_paragraphs is None: - meta_data_paragraphs = len(paragrahps) * [None] - assert len(paragrahps) == len(meta_data_paragraphs) + meta_data_paragraphs = len(paragraphs) * [None] + assert len(paragraphs) == len(meta_data_paragraphs) # convert input to FARM format input_dicts = [] - for paragraph, meta_data in zip(paragrahps, meta_data_paragraphs): + for paragraph, meta_data in zip(paragraphs, meta_data_paragraphs): cur = {"text": paragraph, "questions": [question], "document_id": meta_data["document_id"] @@ -202,6 +202,8 @@ class FARMReader: positive_found = False for a in pred["predictions"][0]["answers"]: # skip "no answers" here + # For now we only take one prediction from each passage + # TODO use more predictions per passage when setting n_candidates_per_passage + make FARM predictions more varied if(not positive_found and a["answer"]): cur = {"answer": a["answer"], "score": a["score"],