01-langchain local embedding-de-en.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8826e5f",
   "metadata": {},
   "source": [
    "# Exploring parallel English German Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09d8b218",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc5f4a8a",
   "metadata": {
    "scrolled": false
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    {
     "name": "stderr",
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     "text": [
      "No sentence-transformers model found with name /home/hfwittmann/.cache/torch/sentence_transformers/T-Systems-onsite_cross-en-de-roberta-sentence-transformer. Creating a new one with MEAN pooling.\n"
     ]
    }
   ],
   "source": [
    "embedding_provider = HuggingFaceEmbeddings(model_name='T-Systems-onsite/cross-en-de-roberta-sentence-transformer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d170e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HuggingFaceEmbeddings(client=SentenceTransformer(\n",
       "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel \n",
       "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
       "), model_name='T-Systems-onsite/cross-en-de-roberta-sentence-transformer', cache_folder=None, model_kwargs={}, encode_kwargs={}, multi_process=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_provider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "501c5b0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: This is a test.\n",
      "\n",
      "Text: Another test.\n",
      "\n",
      "Text: Diese Person ist unglücklich.\n",
      "\n",
      "Text: Diese Person ist glücklich\n",
      "\n",
      "Text: This person is happy.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate embeddings\n",
    "texts = [\"This is a test.\", \"Another test.\", \"Diese Person ist unglücklich.\", \"Diese Person ist glücklich\", \"This person is happy.\"]\n",
    "embeddings = embedding_provider.embed_documents(texts)\n",
    "\n",
    "# Print the embeddings\n",
    "for text, embedding in zip(texts, embeddings):\n",
    "    print(f\"Text: {text}\")\n",
    "    # print(f\"Embedding: {embedding}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508f6a99",
   "metadata": {},
   "source": [
    "# Define cosine similarity by Hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3feb8512",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def cosine_similarity(a, b):\n",
    "    a = np.array(a)\n",
    "    b = np.array(b)\n",
    "    \n",
    "    return a.dot(b) / (a.dot(a) * b.dot(b))**0.5\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f41bce5f",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "cs = cosine_similarity(embeddings[2], embeddings[3])\n",
    "print(cs, embeddings[2], embeddings[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0344fab0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "This is a test. This is a test. similarity: 1.0\n",
      "------------------------------------------------------------\n",
      "This is a test. Another test. similarity: 0.6021323859011859\n",
      "------------------------------------------------------------\n",
      "This is a test. Diese Person ist unglücklich. similarity: 0.0063078595935602885\n",
      "------------------------------------------------------------\n",
      "This is a test. Diese Person ist glücklich similarity: 0.03549630299764126\n",
      "------------------------------------------------------------\n",
      "This is a test. This person is happy. similarity: 0.06479121622443222\n",
      "------------------------------------------------------------\n",
      "Another test. This is a test. similarity: 0.6021323859011859\n",
      "------------------------------------------------------------\n",
      "Another test. Another test. similarity: 1.0\n",
      "------------------------------------------------------------\n",
      "Another test. Diese Person ist unglücklich. similarity: -0.002673047047087005\n",
      "------------------------------------------------------------\n",
      "Another test. Diese Person ist glücklich similarity: -0.0030906702068004797\n",
      "------------------------------------------------------------\n",
      "Another test. This person is happy. similarity: 0.016307584386514504\n",
      "------------------------------------------------------------\n",
      "Diese Person ist unglücklich. This is a test. similarity: 0.0063078595935602885\n",
      "------------------------------------------------------------\n",
      "Diese Person ist unglücklich. Another test. similarity: -0.002673047047087005\n",
      "------------------------------------------------------------\n",
      "Diese Person ist unglücklich. Diese Person ist unglücklich. similarity: 1.0\n",
      "------------------------------------------------------------\n",
      "Diese Person ist unglücklich. Diese Person ist glücklich similarity: 0.3379798948478462\n",
      "------------------------------------------------------------\n",
      "Diese Person ist unglücklich. This person is happy. similarity: 0.3365909556022746\n",
      "------------------------------------------------------------\n",
      "Diese Person ist glücklich This is a test. similarity: 0.03549630299764126\n",
      "------------------------------------------------------------\n",
      "Diese Person ist glücklich Another test. similarity: -0.0030906702068004797\n",
      "------------------------------------------------------------\n",
      "Diese Person ist glücklich Diese Person ist unglücklich. similarity: 0.3379798948478462\n",
      "------------------------------------------------------------\n",
      "Diese Person ist glücklich Diese Person ist glücklich similarity: 1.0\n",
      "------------------------------------------------------------\n",
      "Diese Person ist glücklich This person is happy. similarity: 0.9912835575491499\n",
      "------------------------------------------------------------\n",
      "This person is happy. This is a test. similarity: 0.06479121622443222\n",
      "------------------------------------------------------------\n",
      "This person is happy. Another test. similarity: 0.016307584386514504\n",
      "------------------------------------------------------------\n",
      "This person is happy. Diese Person ist unglücklich. similarity: 0.3365909556022746\n",
      "------------------------------------------------------------\n",
      "This person is happy. Diese Person ist glücklich similarity: 0.9912835575491499\n",
      "------------------------------------------------------------\n",
      "This person is happy. This person is happy. similarity: 1.0\n"
     ]
    }
   ],
   "source": [
    "for text1 in texts:\n",
    "    for text2 in texts:\n",
    "        embeddings1 = embedding_provider.embed_documents([text1])[0]\n",
    "        embeddings2 = embedding_provider.embed_documents([text2])[0]\n",
    "        c_s = cosine_similarity(embeddings1, embeddings2)\n",
    "        print('------------------------------------------------------------')\n",
    "        print(text1, text2, 'similarity:', c_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34fd4240",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "168122f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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