diff --git a/dl_sentiment_classifiers.ipynb b/dl_sentiment_classifiers.ipynb new file mode 100644 index 0000000..d626398 --- /dev/null +++ b/dl_sentiment_classifiers.ipynb @@ -0,0 +1,9081 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X2Pw5uR1SxCB" + }, + "source": [ + "---\n", + "---\n", + "# ⚛**Emotion Analysis - Cancer Narratives**⚛\n", + "---\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B3vU8SJ5CchM" + }, + "outputs": [], + "source": [ + "examples = [\"At first I thought she might make a good recovery because she started eating well and seemed happy.\", \"Going into town I will enjoy the delightful offerings from del taco and a Xanex. We dine like kings!\" ,\"I’m going to die, and I’m going to do it with as much dignity as possible, and have the best last few months I can possibly have.\", \"I made him breakfast, and dinner and tried to keep a positive mindset and just do the happy stuff we always loved doing, jamming, joking, eating waffles.\",\n", + "\"Today I am officially 25 years cancer free.\", \"Hello comrades, just wanted to share my story after a surgery where I scored another goal against cancer and made it 3-0.\", \"I told her I loved her and held her hand while they ended life support.\",\n", + "\"But hearing the phrase, 'you no longer have cancer' makes me feel invincible again.\", \"I’m still battling side effects but I’m so relieved to be done!!\" \"I get to come home to my beautiful wife and loving dog every time.\",\n", + "\"I’m trying to take it all in and enjoy my family and the festivities. There’s so much to enjoy.\", \"I have been told I will never be cancer free, I have learnt to accept that. \", \"Finished a year of treatment and continued on maintenance chemotherapy for another year and was cancer free for a while until I relapsed at 22.\", \"I’ve completed 8 rounds of chemotherapy and 25 sessions of chemoradiation.\", \"This morning was my last day of radiation!\", \"They were able to cut out the tumor but weren't successful in getting clean margins on the first pass.\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WnOFNt9bFTr1", + "outputId": "e4bf9fd7-6ff0-4843-9ebe-6af3b1e9b9a0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['At first I thought she might make a good recovery because she started eating well and seemed happy.',\n", + " 'Going into town I will enjoy the delightful offerings from del taco and a Xanex. We dine like kings!',\n", + " 'I’m going to die, and I’m going to do it with as much dignity as possible, and have the best last few months I can possibly have.',\n", + " 'I made him breakfast, and dinner and tried to keep a positive mindset and just do the happy stuff we always loved doing, jamming, joking, eating waffles.',\n", + " 'Today I am officially 25 years cancer free.',\n", + " 'Hello comrades, just wanted to share my story after a surgery where I scored another goal against cancer and made it 3-0.',\n", + " 'I told her I loved her and held her hand while they ended life support.',\n", + " \"But hearing the phrase, 'you no longer have cancer' makes me feel invincible again.\",\n", + " 'I’m still battling side effects but I’m so relieved to be done!!I get to come home to my beautiful wife and loving dog every time.',\n", + " 'I’m trying to take it all in and enjoy my family and the festivities. There’s so much to enjoy.',\n", + " 'I have been told I will never be cancer free, I have learnt to accept that. ',\n", + " 'Finished a year of treatment and continued on maintenance chemotherapy for another year and was cancer free for a while until I relapsed at 22.',\n", + " 'I’ve completed 8 rounds of chemotherapy and 25 sessions of chemoradiation.',\n", + " 'This morning was my last day of radiation!',\n", + " \"They were able to cut out the tumor but weren't successful in getting clean margins on the first pass.\"]" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "examples" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bXPmDE6KCTSc" + }, + "source": [ + "---\n", + "# **Roberta Go - [Roberta - Go Emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions)**\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345, + "referenced_widgets": [ + "98faae1e3ad0467793eac1e849dcaf41", + "dd44d767fc6e4744b9f4af5326da6e1f", + "e04fd696118a49528b39eaa12207ad10", + "c551e9c47046464f8e25d157bd1e3cc3", + "d93eec3b9acf49bbbee6a809a8a13040", + "02605b03b8274ecb9462a5804282216d", + "e3da3e87e06d46198d7d57e73d3151d1", + "3465f2f80e564b5dab71d9734fef6678", + "341f504c77ee49178171a41e446d5345", + "f31dd4ad075240e4a8c8d8d1894cf33d", + "7e569b62c0814caf9e10672d232fa0fd", + "d959d5394e4e4075a3dee7e719c853c1", + "f1e902b88ec74bd397c8bc107ccc83fa", + "3aab591145d74a81bb6d64dc30faa9b5", + "bee5996ae8334e79a161fc993f260225", + "873b47cf5a0c41858292285eef2178d6", + "923bb107c56c4a6883c6d1d9ed4be555", + "e6d6d18c14454ce2a5594828579180ef", + "8cdd6256012e4b37a8847858ed78cb6c", + "9e104d215c7546dfb3814678938538ad", + "d81695f6190f4a6984aac7e39333d2c8", + "af514a9d95aa47dc916ad6ff64331cc4", + "769bc2c65ede45bca768ff141fda5cf6", + "fd5ebecb9f7a474bbaabefd9c68ade0a", + "d114e2be388a4bb2818137455a18fe2d", + "98f796eac1e245f3bf64a2242b755ef0", + "d59129d9d7974390a176a0f9c7464ba3", + "6b3a751b01744fb998d2736db664d941", + "e7bece9392334c939339f15c54667992", + "ff29c0a5902a430cb90767156f3d1efe", + "4f0947e710384463894e60a1bd860d1d", + "cc5c405891674c388311fe924f3e8661", + "6616f0a8d210460fb6bbdbaa4a554fe8", + "e361f86571bc4fd597928c623599c29b", + "13785a19c21740eea91182c82e0eb1f5", + "21d341a00b944c3d8975d527b3fd4a0f", + "cfae35f64fb746b383d1e44b028902f2", + "29a1d5129df94b9c9e7655bf88fbe819", + "7ef6b0eecc734a76bf1faf6c699f9265", + "1e0c27216fe348d9bd4c53075b3f5804", + "01090fffc1784222a92ca71539df133d", + "5c68705f5bf844b9acc67352113a6e17", + "1998bd5f91324ddea1e2e382b2db9856", + "e04adcf0c95844b6b93e41f956302232", + "9549038180fc4c0cb7dcd77de5659c44", + "43f25e1b5a6c416dbf91f88a42255426", + "f4e83ab1e8f241debf5620ebeb357062", + "ee4c7239d51b44c19d460a13b021629a", + "10cf30ac67f74834a18e4809289df95c", + "cfa7b84fbf2b447db6083409c8bdd85f", + "eb39f148e50946e483a798f4d27469a4", + "5c5496198fbc47f1bae56057fef47bc1", + "f2aa66a04f7a43c292bf723408d5ff2b", + "88ec44dd06294db892bf38595e990258", + "d319cd20bdc14610ada2b7dcfc95cd55", + "3365f99a0e854983ba78a3f2f56b2a3e", + "876cc83bdd9847e69653728ea9d3c4b9", + "4195c62a7b92453abe126d26e374db6f", + "ebb4297837454c21a733816d192cc695", + "8702a0879cf9452bad10a736c2c74acf", + "8b8985daf3de4fba9ce9056d46b16ac5", + "ca7c8b24d72d48ab8657c5841314e126", + "99a0ef2165074c12b85da9a3298907b8", + "1f34a463a3b34cfbb1d66ae6ec92ca80", + "9a066490055c4734a00f59695764c411", + "12b34f84154b4e19a3651dbd3d73bf77", + "09fcd7a0ec8849eba56638e5de740449", + "aa5175a8a81b4709bda87dc82e35faa4", + "5b46e02ee5354c7080033f525860aabb", + "af4e95811b7649408de4e3f0732d3304", + "79d77d71e4184ad59fcbf14a53d29630", + "fd5f3e7c4f7e46159f2d8a2d2c618669", + "00f3dfc7da3d478298fedbc323f487db", + "4ef74c9c8d7844d580d31c4fb0b66d53", + "8fb354d64ca0440ab12878aa1a531a66", + "64af4c41b8544f32a5104d5180d7698c", + "73cde1982b944ab98081e58dac162480" + ] + }, + "id": "kxj-PCNpCQ3y", + "outputId": "fff18fa1-d44c-4f24-fde1-0c1cba9638f3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.92k [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n", + "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/modeling_auto.py:1595: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/892M [00:00', return_tensors='pt')\n", + "\n", + " output = model.generate(input_ids=input_ids,\n", + " max_length=2)\n", + "\n", + " dec = [tokenizer.decode(ids) for ids in output]\n", + " label = dec[0]\n", + " return label\n", + "\n", + "for ex in examples:\n", + " t5.append(get_emotion(ex))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AvNLw5L3GzBt" + }, + "source": [ + "---\n", + "# **DistilBert - [DistilBERT base uncased finetuned SST-2](https://huggingface.co/blog/sentiment-analysis-python)**\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "835c8a85cc58446cb35eb11625bb9c56", + "9f634f77fa2d4711be1d0fa69b563a74", + "78f5e0f22e714d99bbed95b7fee6139e", + "64a38f68401d4e5e951fc64ee5504888", + "86b9d2ce03c24f8289846badfa22ee35", + "184a406239d348f1b3c1946127eb567c", + "ceda05528cc9455ea51356a98604299b", + "ec6aafc6a39c4362b8bbd19a86bf49bd", + "1451205f88304808a72d48fe4f7131fc", + "64f2c1e7d2dc43448d6f8398de23023d", + "5af5de25c3fe4e3b809dfd6417c758b3", + "7cc959208b1f4802b9b93a01a9784a67", + "34af07062e9243109f4241d0e387db36", + "1f44715d488c4862a4d9deaa5ff2c5f1", + "a5d2a3fc55d149f8b76da32fdb85e628", + "b4d64bd4f2eb4caaa3be6e965acbf2c6", + "f6fcc362c9d04773ad3641ecf626daac", + "11d5d51851c244fe91c739ed4da5f7b4", + "366746f842e04a5caf50737637628e26", + "61b165ff119a4ec2a227ea4422428603", + "998cb41402ae49c7966f8b5c1255313f", + "1b66efcbcb3644d3910146b817aea7be", + "9ca883ff6fe140a6a7b7f19f6feedf0c", + "bb65042973db4f92b4b2c6a4ccdebe3f", + "3fb80541efd74e4cb7276a3c647899f8", + "9b58bc690e0b45daa91745fa2487a896", + "5a566c3ad51041e2b40a08469d4659af", + "b3f7e75bae0349559a72d8e15e42a45d", + "8ced9a74df0b4897add4c65ef6b449d1", + "acf80bd701b8450c935b510bb708ff79", + "807b633e48524088809675ff54bcd6c1", + "46c25b88446548d6b36e8389315bf8e2", + "eef4fd3ba3454c6385663384faf0a995", + "7004eea92bdf4cba8cdf821c5aee18f1", + "b87820f92ea94c35a6d2982ed6ddeced", + "562f22d7f4f2460bb73f269859aeaff2", + "fbf7fe6e407a488eb09300536a05f101", + "63c9c49032cf47548dfe99cec771788c", + "9d4bd39697464ed4a8eb1b6a339e71ae", + "8c43f3499d044a9c9190bc8523062ba3", + "7e93e375a4a24f05909e0783046b293c", + "4f7d0f06aa6b49f4af9d652c1605d314", + "2394ae4092ca40be9eebff66b479b3d4", + "698a0a74d0f14931a1d8ac599b5371c8" + ] + }, + "id": "u1z3ASHxGxTN", + "outputId": "5ac3d8da-4a85-4b3c-c752-82ef5f3244af" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/48.0 [00:00 1 else t\n", + " t = 'http' if t.startswith('http') else t\n", + " new_text.append(t)\n", + " return \" \".join(new_text)\n", + "MODEL = f\"cardiffnlp/twitter-roberta-base-sentiment-latest\"\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n", + "config = AutoConfig.from_pretrained(MODEL)\n", + "\n", + "model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n", + "\n", + "roberta = []\n", + "for text in examples:\n", + " text = preprocess(text)\n", + " encoded_input = tokenizer(text, return_tensors='pt')\n", + " output = model(**encoded_input)\n", + " scores = output[0][0].detach().numpy()\n", + " scores = softmax(scores)\n", + " ranking = np.argsort(scores)\n", + " ranking = ranking[::-1]\n", + " for i in range(scores.shape[0]):\n", + " l = config.id2label[ranking[i]]\n", + " s = scores[ranking[i]]\n", + " # roberta.append(f\"{l} {np.round(float(s), 4)}\")\n", + " roberta.append(l)\n", + "\n", + "roberta = [v for i, v in enumerate(roberta) if i%3 == 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qmM8Tsd3G8Qf" + }, + "source": [ + "---\n", + "# **NRCLex - [National Research Council Canada (NRC) affect lexicon](https://pypi.org/project/NRCLex/)**\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kJNVnLgtHekU", + "outputId": "6459c3de-1e9c-47ed-fb30-9c8a9a10d908" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting NRCLex\n", + " Downloading NRCLex-4.0-py3-none-any.whl (4.4 kB)\n", + "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (from NRCLex) (0.17.1)\n", + "INFO: pip is looking at multiple versions of nrclex to determine which version is compatible with other requirements. This could take a while.\n", + " Downloading NRCLex-3.0.0.tar.gz (396 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/396.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.6/396.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m396.4/396.4 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.10/dist-packages (from textblob->NRCLex) (3.8.1)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob->NRCLex) (8.1.7)\n", + "Requirement already satisfied: joblib in 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"metadata": { + "id": "QfDXnbPUHh2y", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "96c4fd94-8f5d-42b7-f2c3-ead095e30341" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Unzipping corpora/stopwords.zip.\n", + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "from nrclex import NRCLex\n", + "import nltk\n", + "nltk.download('stopwords')\n", + "nltk.download('punkt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iutZE_Y1I4QZ" + }, + "outputs": [], + "source": [ + "nrclex = []\n", + "for ex in examples:\n", + " text_object = NRCLex(ex)\n", + " nrclex.append(text_object.top_emotions)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "# **USAS**\n", + "---\n", + "\n" + ], + "metadata": { + "id": "DgooMfOBPQg2" + } + }, + { + "cell_type": "code", + "source": [ + "# Install the PyDrive wrapper & import libraries.\n", + "!pip install import-ipynb\n", + "!pip install -U -q PyDrive" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NBimLKVGQR83", + "outputId": "27033fa7-9e6c-45c1-bbc3-629843f3e501" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting import-ipynb\n", + " Downloading import_ipynb-0.1.4-py3-none-any.whl (4.1 kB)\n", + "Requirement already satisfied: IPython in /usr/local/lib/python3.10/dist-packages (from import-ipynb) (7.34.0)\n", + "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (from import-ipynb) (5.9.2)\n", + "Requirement already satisfied: 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(0.2.13)\n", + "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core->nbformat->import-ipynb) (4.2.0)\n", + "Installing collected packages: jedi, import-ipynb\n", + "Successfully installed import-ipynb-0.1.4 jedi-0.19.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import import_ipynb\n", + "# This only needs to be done once per notebook.\n", + "from pydrive.auth import GoogleAuth\n", + "from pydrive.drive import GoogleDrive\n", + "from google.colab import auth\n", + "from oauth2client.client import GoogleCredentials\n", + "\n", + "# Authenticate and create the PyDrive client.\n", + "# This only needs to be done once per notebook.\n", + "auth.authenticate_user()\n", + "gauth = GoogleAuth()\n", + "gauth.credentials = GoogleCredentials.get_application_default()\n", + "drive = GoogleDrive(gauth)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B08M5Wl8PWez", + "outputId": "5e508348-99a8-49e9-fa5c-2eb629638520" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:root:pydrive is deprecated and no longer maintained. We recommend that you migrate your projects to pydrive2, the maintained fork of pydrive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Copy the link and remove the front part of the link (i.e. https://drive.google.com/open?id=) to get the file ID.\n", + "your_module = drive.CreateFile({'id':'1qd531E2R36wwqGa9wlTZa5MquLKlFFaS'})\n", + "your_module.GetContentFile('usas_sentiment_classifier.ipynb')\n", + "import usas_sentiment_classifier" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0S5u3DSOP6dy", + "outputId": "ac71146c-ec80-4095-82bf-4c79bce7281f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "importing Jupyter notebook from usas_sentiment_classifier.ipynb\n", + "Mounted at /content/gdrive/\n", + "Collecting en-dual-none-contextual==0.3.3\n", + " Downloading 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"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", + "You can now load the package via spacy.load('en_core_web_sm')\n", + "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n", + "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n", + "order to load all the package's dependencies. You can do this by selecting the\n", + "'Restart kernel' or 'Restart runtime' option.\n", + "Collecting colorama\n", + " Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", + "Installing collected packages: colorama\n", + "Successfully installed colorama-0.4.6\n" + ] + }, + { + "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" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "usas_sents = []\n", + "for ex in examples:\n", + " emo_dict = usas_sentiment_classifier.usas_emotion_tags(ex)\n", + " sent, pos, neg, neu = usas_sentiment_classifier.usas_sentiment_classifier(emo_dict, 0, 0, 0)\n", + " probs = usas_sentiment_classifier.text_sentiment_prob(ex, pos, neg, neu)\n", + " usas_sents.append(probs)" + ], + "metadata": { + "id": "dATSwb-CSWUz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "usas_sents" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2VOq4GGcV0km", + "outputId": "d3aaed3f-0b3e-4194-a71a-e96c3381696a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['positive=0.5, negative=0.5, neutral=0.0',\n", + " 'positive=0.6666666666666666, negative=0.0, neutral=0.3333333333333333',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.8, negative=0.2, neutral=0.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.5, negative=0.5, neutral=0.0',\n", + " 'positive=0.6666666666666666, negative=0.0, neutral=0.3333333333333333',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=0.0, neutral=1.0',\n", + " 'positive=0.0, negative=1.0, neutral=0.0']" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "# **Comparative Analysis**\n", + "---" + ], + "metadata": { + "id": "Q3BpdhXCPwMZ" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fd2ByFd6KsqJ" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "eval_set = pd.DataFrame()\n", + "eval_set['Sentences'] = examples\n", + "eval_set['USAS'] = usas_sents\n", + "eval_set['Roberta'] = roberta\n", + "eval_set['DistilBERT'] = distil\n", + "eval_set['NRCLex'] = nrclex\n", + "eval_set['T5'] = t5\n", + "eval_set['RobertaGo'] = robertaGo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kx6p93hqNVdZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 762 + }, + "outputId": "cca2886e-4e50-4146-cdb2-e75de081433d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Sentences \\\n", + "0 At first I thought she might make a good recov... \n", + "1 Going into town I will enjoy the delightful of... \n", + "2 I’m going to die, and I’m going to do it with ... \n", + "3 I made him breakfast, and dinner and tried to ... \n", + "4 Today I am officially 25 years cancer free. \n", + "5 Hello comrades, just wanted to share my story ... \n", + "6 I told her I loved her and held her hand while... \n", + "7 But hearing the phrase, 'you no longer have ca... \n", + "8 I’m still battling side effects but I’m so rel... \n", + "9 I’m trying to take it all in and enjoy my fami... \n", + "10 I have been told I will never be cancer free, ... \n", + "11 Finished a year of treatment and continued on ... \n", + "12 I’ve completed 8 rounds of chemotherapy and 25... \n", + "13 This morning was my last day of radiation! \n", + "14 They were able to cut out the tumor but weren'... \n", + "\n", + " USAS Roberta DistilBERT \\\n", + "0 positive=0.5, negative=0.5, neutral=0.0 positive POSITIVE \n", + "1 positive=0.6666666666666666, negative=0.0, neu... positive POSITIVE \n", + "2 positive=0.0, negative=0.0, neutral=1.0 positive POSITIVE \n", + "3 positive=0.8, negative=0.2, neutral=0.0 positive POSITIVE \n", + "4 positive=0.0, negative=0.0, neutral=1.0 positive POSITIVE \n", + "5 positive=0.0, negative=0.0, neutral=1.0 positive POSITIVE \n", + "6 positive=0.0, negative=0.0, neutral=1.0 positive POSITIVE \n", + "7 positive=0.0, negative=0.0, neutral=1.0 positive POSITIVE \n", + "8 positive=0.5, negative=0.5, neutral=0.0 positive POSITIVE \n", + "9 positive=0.6666666666666666, negative=0.0, neu... positive POSITIVE \n", + "10 positive=0.0, negative=0.0, neutral=1.0 negative POSITIVE \n", + "11 positive=0.0, negative=0.0, neutral=1.0 neutral NEGATIVE \n", + "12 positive=0.0, negative=0.0, neutral=1.0 neutral NEGATIVE \n", + "13 positive=0.0, negative=0.0, neutral=1.0 positive NEGATIVE \n", + "14 positive=0.0, negative=1.0, neutral=0.0 neutral NEGATIVE \n", + "\n", + " NRCLex T5 \\\n", + "0 [(positive, 0.2727272727272727), (anticipation... joy \n", + "1 [(trust, 0.25), (positive, 0.25), (joy, 0.25),... joy \n", + "2 [(fear, 0.2), (trust, 0.2), (positive, 0.2), (... joy \n", + "3 [(positive, 0.5714285714285714)] joy \n", + "4 [(fear, 0.2), (anger, 0.2), (negative, 0.2), (... joy \n", + "5 [(fear, 0.18181818181818182), (sadness, 0.1818... joy \n", + "6 [(fear, 0.0), (anger, 0.0), (anticip, 0.0), (t... love \n", + "7 [(fear, 0.2857142857142857), (negative, 0.2857... joy \n", + "8 [(positive, 0.3333333333333333), (joy, 0.33333... joy \n", + "9 [(trust, 0.25), (positive, 0.25), (joy, 0.25),... joy \n", + "10 [(fear, 0.2), (anger, 0.2), (negative, 0.2), (... joy \n", + "11 [(fear, 0.16666666666666666), (anger, 0.166666... joy \n", + "12 [(fear, 0.0), (anger, 0.0), (anticip, 0.0), (t... joy \n", + "13 [(fear, 0.5), (negative, 0.5)] sadness \n", + "14 [(trust, 0.2222222222222222), (positive, 0.222... joy \n", + "\n", + " RobertaGo \n", + "0 {'label': 'joy', 'score': 0.7537976503372192} \n", + "1 {'label': 'joy', 'score': 0.8973855972290039} \n", + "2 {'label': 'optimism', 'score': 0.6296710968017... \n", + "3 {'label': 'joy', 'score': 0.6845877170562744} \n", + "4 {'label': 'joy', 'score': 0.3095731735229492} \n", + "5 {'label': 'desire', 'score': 0.3054072856903076} \n", + "6 {'label': 'love', 'score': 0.90290766954422} \n", + "7 {'label': 'joy', 'score': 0.3020244836807251} \n", + "8 {'label': 'joy', 'score': 0.7250108122825623} \n", + "9 {'label': 'joy', 'score': 0.8999956250190735} \n", + "10 {'label': 'approval', 'score': 0.4906566739082... \n", + "11 {'label': 'neutral', 'score': 0.596493124961853} \n", + "12 {'label': 'neutral', 'score': 0.602870762348175} \n", + "13 {'label': 'excitement', 'score': 0.48605513572... \n", + "14 {'label': 'neutral', 'score': 0.680145263671875} " + ], + "text/html": [ + "\n", + "
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SentencesUSASRobertaDistilBERTNRCLexT5RobertaGo
0At first I thought she might make a good recov...positive=0.5, negative=0.5, neutral=0.0positivePOSITIVE[(positive, 0.2727272727272727), (anticipation...<pad> joy{'label': 'joy', 'score': 0.7537976503372192}
1Going into town I will enjoy the delightful of...positive=0.6666666666666666, negative=0.0, neu...positivePOSITIVE[(trust, 0.25), (positive, 0.25), (joy, 0.25),...<pad> joy{'label': 'joy', 'score': 0.8973855972290039}
2I’m going to die, and I’m going to do it with ...positive=0.0, negative=0.0, neutral=1.0positivePOSITIVE[(fear, 0.2), (trust, 0.2), (positive, 0.2), (...<pad> joy{'label': 'optimism', 'score': 0.6296710968017...
3I made him breakfast, and dinner and tried to ...positive=0.8, negative=0.2, neutral=0.0positivePOSITIVE[(positive, 0.5714285714285714)]<pad> joy{'label': 'joy', 'score': 0.6845877170562744}
4Today I am officially 25 years cancer free.positive=0.0, negative=0.0, neutral=1.0positivePOSITIVE[(fear, 0.2), (anger, 0.2), (negative, 0.2), (...<pad> joy{'label': 'joy', 'score': 0.3095731735229492}
5Hello comrades, just wanted to share my story ...positive=0.0, negative=0.0, neutral=1.0positivePOSITIVE[(fear, 0.18181818181818182), (sadness, 0.1818...<pad> joy{'label': 'desire', 'score': 0.3054072856903076}
6I told her I loved her and held her hand while...positive=0.0, negative=0.0, neutral=1.0positivePOSITIVE[(fear, 0.0), (anger, 0.0), (anticip, 0.0), (t...<pad> love{'label': 'love', 'score': 0.90290766954422}
7But hearing the phrase, 'you no longer have ca...positive=0.0, negative=0.0, neutral=1.0positivePOSITIVE[(fear, 0.2857142857142857), (negative, 0.2857...<pad> joy{'label': 'joy', 'score': 0.3020244836807251}
8I’m still battling side effects but I’m so rel...positive=0.5, negative=0.5, neutral=0.0positivePOSITIVE[(positive, 0.3333333333333333), (joy, 0.33333...<pad> joy{'label': 'joy', 'score': 0.7250108122825623}
9I’m trying to take it all in and enjoy my fami...positive=0.6666666666666666, negative=0.0, neu...positivePOSITIVE[(trust, 0.25), (positive, 0.25), (joy, 0.25),...<pad> joy{'label': 'joy', 'score': 0.8999956250190735}
10I have been told I will never be cancer free, ...positive=0.0, negative=0.0, neutral=1.0negativePOSITIVE[(fear, 0.2), (anger, 0.2), (negative, 0.2), (...<pad> joy{'label': 'approval', 'score': 0.4906566739082...
11Finished a year of treatment and continued on ...positive=0.0, negative=0.0, neutral=1.0neutralNEGATIVE[(fear, 0.16666666666666666), (anger, 0.166666...<pad> joy{'label': 'neutral', 'score': 0.596493124961853}
12I’ve completed 8 rounds of chemotherapy and 25...positive=0.0, negative=0.0, neutral=1.0neutralNEGATIVE[(fear, 0.0), (anger, 0.0), (anticip, 0.0), (t...<pad> joy{'label': 'neutral', 'score': 0.602870762348175}
13This morning was my last day of radiation!positive=0.0, negative=0.0, neutral=1.0positiveNEGATIVE[(fear, 0.5), (negative, 0.5)]<pad> sadness{'label': 'excitement', 'score': 0.48605513572...
14They were able to cut out the tumor but weren'...positive=0.0, negative=1.0, neutral=0.0neutralNEGATIVE[(trust, 0.2222222222222222), (positive, 0.222...<pad> joy{'label': 'neutral', 'score': 0.680145263671875}
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "eval_set", + "summary": "{\n \"name\": \"eval_set\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Sentences\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"I\\u2019m trying to take it all in and enjoy my family and the festivities. There\\u2019s so much to enjoy.\",\n \"Finished a year of treatment and continued on maintenance chemotherapy for another year and was cancer free for a while until I relapsed at 22.\",\n \"At first I thought she might make a good recovery because she started eating well and seemed happy.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"USAS\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"positive=0.6666666666666666, negative=0.0, neutral=0.3333333333333333\",\n \"positive=0.0, negative=1.0, neutral=0.0\",\n \"positive=0.0, negative=0.0, neutral=1.0\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Roberta\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"positive\",\n \"negative\",\n \"neutral\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"DistilBERT\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"NEGATIVE\",\n \"POSITIVE\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"NRCLex\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"T5\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \" joy\",\n \" love\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"RobertaGo\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "eval_set" + ] + }, + { + "source": [ + "# @title DistilBERT\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "eval_set.groupby('DistilBERT').size().plot(kind='barh', color=sns.palettes.mpl_palette(\"viridis\", 2))\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "id": "Fgwlah9pZpIM", + "outputId": "141a0c90-69c8-40b1-de8c-f3bd1703c5cb" + } + }, + { + "cell_type": "code", + "source": [ + "# @title ROBERTA\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "eval_set.groupby('Roberta').size().plot(kind='barh', color=sns.mpl_palette(\"viridis\", 3))\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "id": "TeaVxnShah7Z", + "outputId": "fc2a8a54-196d-4f16-9d7c-f7d25277b20c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "source": [ + "# @title USAS\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "eval_set.groupby('USAS').size().plot(kind='barh', color=sns.palettes.mpl_palette(\"viridis\", 5))\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "id": "mZi233d9ZfCS", + "outputId": "5be225c0-c24d-4fe3-e87d-d53b6fec1aa1" + } + }, + { + "source": [ + "# @title T5\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "eval_set.groupby('T5').size().plot(kind='barh', color=sns.mpl_palette(\"viridis\", 3))\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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