DensePassageRetriever: Add Training, Refactor Inference to FARM modul…

…es (#527) * dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * mypy errors fix * DPR instantiation bugfix * Fix DPR init in RAG Tutorial Co-authored-by: Malte Pietsch <[email protected]>
deepset-ai · Oct 30, 2020 · 72b637a · 72b637a
1 parent f134430
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Showing 9 changed files with 420 additions and 383 deletions.
diff --git a/README.rst b/README.rst
@@ -159,8 +159,11 @@ Example
 .. code-block:: python
 
  retriever = DensePassageRetriever(document_store=document_store,
- embedding_model="dpr-bert-base-nq",
- do_lower_case=True, use_gpu=True)
+ query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
+ passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
+ use_gpu=True,
+ batch_size=16,
+ embed_title=True)
  retriever.retrieve(query="Why did the revenue increase?")
  # returns: [Document, Document]
 

diff --git a/haystack/retriever/dense.py b/haystack/retriever/dense.py
diff --git a/requirements.txt b/requirements.txt
@@ -21,4 +21,4 @@ tika
 uvloop; sys_platform != 'win32' and sys_platform != 'cygwin'
 httptools
 nltk
-more_itertools
+more_itertools
diff --git a/test/conftest.py b/test/conftest.py
@@ -180,7 +180,7 @@ def dpr_retriever(faiss_document_store):
  passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
  use_gpu=False,
  embed_title=True,
- remove_sep_tok_from_untitled_passages=True
+ use_fast_tokenizers=True
  )
 
 
@@ -288,14 +288,10 @@ def get_document_store(document_store_type, faiss_document_store, inmemory_docum
 def get_retriever(retriever_type, document_store):
 
  if retriever_type == "dpr":
- retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=False,
- embed_title=True,
- remove_sep_tok_from_untitled_passages=True
- )
+ retriever = DensePassageRetriever(document_store=document_store,
+ query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
+ passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
+ use_gpu=False, embed_title=True)
  elif retriever_type == "tfidf":
  return TfidfRetriever(document_store=document_store)
  elif retriever_type == "embedding":

diff --git a/test/test_dpr_retriever.py b/test/test_dpr_retriever.py
@@ -45,11 +45,11 @@ def test_dpr_retrieval(document_store, retriever, return_embedding):
  # FAISSDocumentStore doesn't return embeddings, so these tests only work with ElasticsearchDocumentStore
  if isinstance(document_store, ElasticsearchDocumentStore):
  assert (len(docs_with_emb[0].embedding) == 768)
- assert (abs(docs_with_emb[0].embedding[0] - (-0.30634)) < 0.001)
- assert (abs(docs_with_emb[1].embedding[0] - (-0.37449)) < 0.001)
- assert (abs(docs_with_emb[2].embedding[0] - (-0.24695)) < 0.001)
- assert (abs(docs_with_emb[3].embedding[0] - (-0.08017)) < 0.001)
- assert (abs(docs_with_emb[4].embedding[0] - (-0.01534)) < 0.001)
+ assert (abs(docs_with_emb[0].embedding[0] - (-0.3063)) < 0.001)
+ assert (abs(docs_with_emb[1].embedding[0] - (-0.3914)) < 0.001)
+ assert (abs(docs_with_emb[2].embedding[0] - (-0.2470)) < 0.001)
+ assert (abs(docs_with_emb[3].embedding[0] - (-0.0802)) < 0.001)
+ assert (abs(docs_with_emb[4].embedding[0] - (-0.0551)) < 0.001)
 
  res = retriever.retrieve(query="Which philosopher attacked Schopenhauer?")
 

diff --git a/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb b/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb
@@ -515,11 +515,12 @@
  "retriever = DensePassageRetriever(document_store=document_store,\n",
  " query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
  " passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
+ " max_seq_len_query=64,\n",
+ " max_seq_len_passage=256,\n",
+ " batch_size=16,\n",
  " use_gpu=True,\n",
  " embed_title=True,\n",
- " max_seq_len=256,\n",
- " batch_size=16,\n",
- " remove_sep_tok_from_untitled_passages=True)\n",
+ " use_fast_tokenizers=True)\n",
  "# Important: \n",
  "# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all\n",
  "# previously indexed documents and update their embedding representation. \n",

diff --git a/tutorials/Tutorial6_Better_Retrieval_via_DPR.py b/tutorials/Tutorial6_Better_Retrieval_via_DPR.py
@@ -29,9 +29,13 @@
 retriever = DensePassageRetriever(document_store=document_store,
  query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
  passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
+ max_seq_len_query=64,
+ max_seq_len_passage=256,
+ batch_size=2,
  use_gpu=True,
  embed_title=True,
- remove_sep_tok_from_untitled_passages=True)
+ use_fast_tokenizers=True
+ )
 
 # Important:
 # Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all