This Snippet of code demonstates cross-language sentence embeddings system used for similarity search & match beating LASER embeddings [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) by Nils Reimers and Iryna Gurevych.

sentence_similarity_mult.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!git clone [email protected]:UKPLab/sentence-transformers.git\n",
    "!cd sentence-transformers\n",
    "!pip install ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "from sentence_transformers import models, SentenceTransformer\n",
    "\n",
    "model = SentenceTransformer('distiluse-base-multilingual-cased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corpus with example sentences, some sentences were swapped to French, Italian and German\n",
    "corpus = ['Un homme mange de la nourriture.', #FR 'A man is eating food.',\n",
    "          'A man is eating a piece of bread.',\n",
    "          'Das Mädchen trägt ein Baby.', #DE 'The girl is carrying a baby.',\n",
    "          'A man is riding a horse.',\n",
    "          'An elderly man is enjoying dinner.',\n",
    "          'Amis partageant du vin dans un restaurant.', #FR 'Friends sharing wine at a restaurant.',\n",
    "          'A woman is playing violin.',\n",
    "          'A child is learning to play a base guitar.',\n",
    "          'Due uomini hanno spinto i carrelli attraverso i boschi.', #IT 'Two men pushed carts through the woods.',\n",
    "          'A man is riding a white horse on an enclosed ground.',\n",
    "          'Una scimmia suona la batteria.', #IT 'A monkey is playing drums.',\n",
    "          'A cheetah is running behind its prey.']\n",
    "\n",
    "corpus_embeddings = model.encode(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample queries to find similar sentences to, some sentences were translated to Russian and German.\n",
    "queries = ['A man is eating pasta.', \n",
    "           'Кто-то в костюме гориллы играет на барабане', #RU 'Someone in a gorilla costume is playing a set of drums.', \n",
    "           'Ein Gepard jagt Beute über ein Feld.'] #DE 'A cheetah chases prey on across a field.']\n",
    "query_embeddings = model.encode(queries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Cosine similarity of query against each sentence i\n",
    "closest_n = 3\n",
    "for query, query_embedding in zip(queries, query_embeddings):\n",
    "    distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, \"cosine\")[0]\n",
    "\n",
    "    results = zip(range(len(distances)), distances)\n",
    "    results = sorted(results, key=lambda x: x[1])\n",
    "\n",
    "    print(\"\\n======================\\n\")\n",
    "    print(\"Query:\", query)\n",
    "    print(\"\\nTop 3 most similar sentences in corpus:\")\n",
    "\n",
    "    for idx, distance in results[0:closest_n]:\n",
    "        print(corpus[idx].strip(), \"(Score: %.4f)\" % (1-distance))"
   ]
  }
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