Final_Comparison_Sinha.ipynb

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        "The file 'test_sent.txt' is associated with a runtime and is deleted when one terminates. This file, along with the results of this analysis ('BERT-similarities.txt' and 'GloVe-similarities.txt') can be found on Canvas.\n",
        "\n",
        "From the data obtained in 'Final-Word-Embeddings-Sinha.ipynb', we test the similarities between 'words' that apparently conform to the same definition. For instance, 'smh' and 'shaking my head' might superficially mean the same thing however, from our analysis below, it is found that these words do not have the same connotations. \n",
        "\n",
        "For the purpose of looking at the contextual meanings of these words/acronyms/phrases, we use some test sentences from 'tumblr.txt'. The sentences are identical, except apparent synonyms are replaced in this instance to test what kind of a difference this makes to the meanings of the sentences. We consider two different pre-trained models - BERT (for contextual embeddings) and GloVe (for non-contextual embeddings). While these models approach the problem differently, they yield similar results - however, GloVe obviously finds a greater degree of similarity between sentences given its non-contextual approach.\n",
        "\n",
        "The goal of this exercise is to bring to light a pertinent problem in translation and other aspects of natural language processing, and even manual linguistic analysis - connotations. The existence of the same implies that the same word (or equivalently, synonyms) can convey different meanings depending on the context. The findings are discussed in greater detail in the final paper.\n"
      ],
      "metadata": {
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      "source": [
        "# Install sentence_transformers\n",
        "!pip install sentence_transformers"
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        "id": "8edNRaXtYzJ3",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence_transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting transformers<5.0.0,>=4.6.0 (from sentence_transformers)\n",
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            "Collecting sentencepiece (from sentence_transformers)\n",
            "  Downloading sentencepiece-0.1.98-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting huggingface-hub>=0.4.0 (from sentence_transformers)\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (3.12.0)\n",
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            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=4578b45b316ed27b8c2cef5d9dd3ae0a375f8daaaebefcaaacde1f77dfa4bf60\n",
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            "Successfully built sentence_transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence_transformers\n",
            "Successfully installed huggingface-hub-0.14.1 sentence_transformers-2.2.2 sentencepiece-0.1.98 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmJVShUOYmHi",
        "outputId": "8ef57649-a6b5-49e3-a6ba-43f0f6e3df01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Import useful libraries\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The following code is from Practicum 3\n",
        "# Takes in a string of text and returns a list of cleaned tokens\n",
        "def clean_text(text):\n",
        "  tokens = nltk.word_tokenize(text)\n",
        "  pattern = re.compile(\"[a-zA-Z0-9']\")\n",
        "  clean_tokens = [tok.lower() for tok in tokens if pattern.match(tok)]\n",
        "  return clean_tokens"
      ],
      "metadata": {
        "id": "ofVs-iygZkQE"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reads the contents of the file 'test_sent.txt'\n",
        "with open('test_sent.txt') as f:\n",
        "  text = f.read()\n",
        "\n",
        "# Splits the text into posts\n",
        "sents = text.split('\\n')\n",
        "\n",
        "# Splits the texts into two lists. Corresponding indices of the lists are to\n",
        "# be compared below.\n",
        "sent_1 = []\n",
        "sent_2 = []\n",
        "\n",
        "# Alternately places the test sentences in each of the lists under consideration.\n",
        "for i in range(len(sents)):\n",
        "  if i % 2 == 0:\n",
        "    sent_1.append(' '.join(clean_text(sents[i])))\n",
        "  else:\n",
        "    sent_2.append(' '.join(clean_text(sents[i])))"
      ],
      "metadata": {
        "id": "MCJd9dJ9ZaPu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loads the pre-trained BERT model.\n",
        "bert_model = SentenceTransformer('stsb-bert-large')"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1298693e90fd42638a69c8deb81043a8"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The following code is adapted from:\n",
        "# https://www.sbert.net/docs/usage/semantic_textual_similarity.html\n",
        "# Creates sentence embeddings for the test sentences.\n",
        "bert_embeddings1 = bert_model.encode(sent_1, convert_to_tensor=True)\n",
        "bert_embeddings2 = bert_model.encode(sent_2, convert_to_tensor=True)\n",
        "\n",
        "# Computes cosine-similarities.\n",
        "cosine_scores = util.cos_sim(bert_embeddings1, bert_embeddings2)\n",
        "\n",
        "# Prints the pairs and their scores.\n",
        "for i in range(len(sent_1)):\n",
        "    print(\"{}\\n{}\\nScore: {:.4f}\\n\".format(sent_1[i], sent_2[i], cosine_scores[i][i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvzRDlbMtzZO",
        "outputId": "56aa21bb-1c8d-4f3e-afed-4550d7e1dbf0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rethinking my policy on sharing 'help ' posts now meanwhile my leg is literally snapped in half smh\n",
            "rethinking my policy on sharing 'help ' posts now meanwhile my leg is literally snapped in half shaking my head\n",
            "Score: 0.9463\n",
            "\n",
            "smh the disrespect\n",
            "shaking my head the disrespect\n",
            "Score: 0.8274\n",
            "\n",
            "smh i have so much to say but i just wo n't i 'll just unfollow for now\n",
            "shaking my head i have so much to say but i just wo n't i 'll just unfollow for now\n",
            "Score: 0.9081\n",
            "\n",
            "smh why am i like this\n",
            "shaking my head why am i like this\n",
            "Score: 0.7687\n",
            "\n",
            "lol haha\n",
            "lol lmao\n",
            "Score: 0.8482\n",
            "\n",
            "haha lol\n",
            "haha lmao\n",
            "Score: 0.7877\n",
            "\n",
            "alright im done being sappy now haha\n",
            "alright enough being sappy now haha\n",
            "Score: 0.8967\n",
            "\n",
            "maybe leon should smoke weed instead\n",
            "maybe leon should smoke marijuana instead\n",
            "Score: 0.9564\n",
            "\n",
            "like wow i met you on livejournal when i was 14 hi girlie they wouldnt recognize me and idk if theyd remember me if i asked so here we are\n",
            "like wow i met you on livejournal when i was 14 hi girlie they wouldnt recognize me and i do n't know if theyd remember me if i asked so here we are\n",
            "Score: 0.9937\n",
            "\n",
            "ignore me or smth idk\n",
            "ignore me or smth i do n't know\n",
            "Score: 0.8553\n",
            "\n",
            "cheugy trends\n",
            "cringe trends\n",
            "Score: 0.5438\n",
            "\n",
            "sus no way\n",
            "suspicious no way\n",
            "Score: 0.4733\n",
            "\n",
            "sus imposter\n",
            "suspicious imposter\n",
            "Score: 0.5670\n",
            "\n",
            "cause she slang it like that then calling it mid sheesh you know how many people i got into it with over that pigeon\n",
            "cause she slang it like that then calling it average sheesh you know how many people i got into it with over that pigeon\n",
            "Score: 0.9056\n",
            "\n",
            "cause she slang it like that then calling it mid sheesh you know how many people i got into it with over that pigeon\n",
            "cause she slang it like that then calling it basic sheesh you know how many people i got into it with over that pigeon\n",
            "Score: 0.9060\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loads the pre-trained GloVe model.\n",
        "glove_model = SentenceTransformer('average_word_embeddings_glove.6B.300d')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
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      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
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            ],
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    {
      "cell_type": "code",
      "source": [
        "# The following code is adapted from:\n",
        "# https://www.sbert.net/docs/usage/semantic_textual_similarity.html\n",
        "# Creates sentence embeddings for the test sentences.\n",
        "glove_embeddings1 = glove_model.encode(sent_1, convert_to_tensor=True)\n",
        "glove_embeddings2 = glove_model.encode(sent_2, convert_to_tensor=True)\n",
        "\n",
        "# Computes cosine-similarities.\n",
        "cosine_scores = util.cos_sim(glove_embeddings1, glove_embeddings2)\n",
        "\n",
        "# Prints the pairs and their scores.\n",
        "for i in range(len(sent_1)):\n",
        "    print(\"{}\\n{}\\nScore: {:.4f}\\n\".format(sent_1[i], sent_2[i], cosine_scores[i][i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHNK7aksvwJf",
        "outputId": "ce8bd482-ab5d-49e7-ed9f-5f844fd21c8c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rethinking my policy on sharing 'help ' posts now meanwhile my leg is literally snapped in half smh\n",
            "rethinking my policy on sharing 'help ' posts now meanwhile my leg is literally snapped in half shaking my head\n",
            "Score: 0.9186\n",
            "\n",
            "smh the disrespect\n",
            "shaking my head the disrespect\n",
            "Score: 0.3602\n",
            "\n",
            "smh i have so much to say but i just wo n't i 'll just unfollow for now\n",
            "shaking my head i have so much to say but i just wo n't i 'll just unfollow for now\n",
            "Score: 0.8602\n",
            "\n",
            "smh why am i like this\n",
            "shaking my head why am i like this\n",
            "Score: 0.3378\n",
            "\n",
            "lol haha\n",
            "lol lmao\n",
            "Score: 0.7924\n",
            "\n",
            "haha lol\n",
            "haha lmao\n",
            "Score: 0.7893\n",
            "\n",
            "alright im done being sappy now haha\n",
            "alright enough being sappy now haha\n",
            "Score: 0.8801\n",
            "\n",
            "maybe leon should smoke weed instead\n",
            "maybe leon should smoke marijuana instead\n",
            "Score: 0.8807\n",
            "\n",
            "like wow i met you on livejournal when i was 14 hi girlie they wouldnt recognize me and idk if theyd remember me if i asked so here we are\n",
            "like wow i met you on livejournal when i was 14 hi girlie they wouldnt recognize me and i do n't know if theyd remember me if i asked so here we are\n",
            "Score: 0.9731\n",
            "\n",
            "ignore me or smth idk\n",
            "ignore me or smth i do n't know\n",
            "Score: 0.6411\n",
            "\n",
            "cheugy trends\n",
            "cringe trends\n",
            "Score: 0.6876\n",
            "\n",
            "sus no way\n",
            "suspicious no way\n",
            "Score: 0.3194\n",
            "\n",
            "sus imposter\n",
            "suspicious imposter\n",
            "Score: 0.2933\n",
            "\n",
            "cause she slang it like that then calling it mid sheesh you know how many people i got into it with over that pigeon\n",
            "cause she slang it like that then calling it average sheesh you know how many people i got into it with over that pigeon\n",
            "Score: 0.9680\n",
            "\n",
            "cause she slang it like that then calling it mid sheesh you know how many people i got into it with over that pigeon\n",
            "cause she slang it like that then calling it basic sheesh you know how many people i got into it with over that pigeon\n",
            "Score: 0.9598\n",
            "\n"
          ]
        }
      ]
    }
  ]
}