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Update faq model (#1401)

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Branden Chan 2021-09-01 18:39:06 +02:00 committed by GitHub
parent e4c3c3d423
commit 980d88a0f2
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2 changed files with 4 additions and 4 deletions
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4
tutorials/Tutorial4_FAQ_style_QA.ipynb
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@ -162,7 +162,7 @@
"document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\",\n", "document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\",\n",
" index=\"document\",\n", " index=\"document\",\n",
" embedding_field=\"question_emb\",\n", " embedding_field=\"question_emb\",\n",
" embedding_dim=768,\n", " embedding_dim=384,\n",
" excluded_meta_data=[\"question_emb\"])" " excluded_meta_data=[\"question_emb\"])"
] ]
}, },
@ -182,7 +182,7 @@
"execution_count": null, "execution_count": null,
"outputs": [], "outputs": [],
"source": [ "source": [
"retriever = EmbeddingRetriever(document_store=document_store, embedding_model=\"deepset/sentence_bert\", use_gpu=True)" "retriever = EmbeddingRetriever(document_store=document_store, embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\", use_gpu=True)"
], ],
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,

4
tutorials/Tutorial4_FAQ_style_QA.py
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@ -35,7 +35,7 @@ def tutorial4_faq_style_qa():
document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
index="document", index="document",
embedding_field="question_emb", embedding_field="question_emb",
embedding_dim=768, embedding_dim=384,
excluded_meta_data=["question_emb"], excluded_meta_data=["question_emb"],
similarity="cosine") similarity="cosine")
@ -43,7 +43,7 @@ def tutorial4_faq_style_qa():
# Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones). # Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
# We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings. # We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
# #
retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True) retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2", use_gpu=True)
# Download a csv containing some FAQ data # Download a csv containing some FAQ data
# Here: Some question-answer pairs related to COVID-19 # Here: Some question-answer pairs related to COVID-19