TweetEval_emoji.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dc2ONrUUi87F"
   },
   "source": [
    "# Usage of TweetEval and Twitter-specific RoBERTa models\n",
    "\n",
    "In this notebook we show how to perform tasks such as masked language modeling, computing tweet similarity or tweet classificationo using our Twitter-specific RoBERTa models.\n",
    "\n",
    "- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf)\n",
    "- Authors: Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa-Anke and Leonardo Neves.\n",
    "- [Github](https://github.com/cardiffnlp/tweeteval)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J-FFwHN0jj3O"
   },
   "source": [
    "## Preliminaries\n",
    "\n",
    "We define a function to normalize a tweet to the format we used for TweetEval. Note that preprocessing is minimal (replacing user names by `@user` and links by `http`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "gKE0LowmYpTY"
   },
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    new_text = []\n",
    "    for t in text.split(\" \"):\n",
    "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
    "        t = 'http' if t.startswith('http') else t\n",
    "        new_text.append(t)\n",
    "    return \" \".join(new_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JJ480-5vkRNR"
   },
   "source": [
    "We only need to install one dependnecy: the `transformers` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "42j4PR6vYrfg",
    "outputId": "0e806f16-5f58-4afa-e0d7-94d0bccaadc6"
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in c:\\programdata\\anaconda3\\lib\\site-packages (4.12.5)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (0.1.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (6.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (21.0)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (1.20.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (4.62.3)\n",
      "Requirement already satisfied: sacremoses in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (0.0.46)\n",
      "Requirement already satisfied: filelock in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (3.3.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (2021.8.3)\n",
      "Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (2.26.0)\n",
      "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from transformers) (0.10.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from packaging>=20.0->transformers) (3.0.4)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from tqdm>=4.27->transformers) (0.4.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->transformers) (3.2)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->transformers) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->transformers) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->transformers) (1.26.7)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from sacremoses->transformers) (1.1.0)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from sacremoses->transformers) (8.0.3)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from sacremoses->transformers) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "us58XJGyYpTS"
   },
   "source": [
    "## Computing Tweet Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AZq3Q5kKYpTg",
    "outputId": "a2d34be8-eda7-4e47-c281-ddf72fd18ea6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias']\n",
      "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar to:  The book was awesome\n",
      "----------------------------------------\n",
      "1 The movie was great\n",
      "2 Just finished reading 'Embeddings in NLP'\n",
      "3 I just ordered fried chicken 🐣\n",
      "4 What time is the next game?\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModel, TFAutoModel\n",
    "import numpy as np\n",
    "from scipy.spatial.distance import cosine\n",
    "from collections import defaultdict\n",
    "\n",
    "MODEL = \"cardiffnlp/twitter-roberta-base\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
    "model = AutoModel.from_pretrained(MODEL)\n",
    "\n",
    "def get_embedding(text):\n",
    "  text = preprocess(text)\n",
    "  encoded_input = tokenizer(text, return_tensors='pt')\n",
    "  features = model(**encoded_input)\n",
    "  features = features[0].detach().cpu().numpy() \n",
    "  features_mean = np.mean(features[0], axis=0) \n",
    "  return features_mean\n",
    "\n",
    "query = \"The book was awesome\"\n",
    "\n",
    "tweets = [\"I just ordered fried chicken 🐣\", \n",
    "          \"The movie was great\", \n",
    "          \"What time is the next game?\", \n",
    "          \"Just finished reading 'Embeddings in NLP'\"]\n",
    "\n",
    "d = defaultdict(int)\n",
    "for tweet in tweets:\n",
    "  sim = 1-cosine(get_embedding(query),get_embedding(tweet))\n",
    "  d[tweet] = sim\n",
    "\n",
    "print('Most similar to: ',query)\n",
    "print('----------------------------------------')\n",
    "for idx,x in enumerate(sorted(d.items(), key=lambda x:x[1], reverse=True)):\n",
    "  print(idx+1,x[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oI6B4lOekYeJ"
   },
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0HALJ3ULdAaK",
    "outputId": "d261cb64-02fb-42de-ab67-8b74cd754a9d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias']\n",
      "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(768,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModel, TFAutoModel\n",
    "import numpy as np\n",
    "\n",
    "MODEL = \"cardiffnlp/twitter-roberta-base\"\n",
    "text = \"Good night 😊\"\n",
    "text = preprocess(text)\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
    "\n",
    "encoded_input = tokenizer(text, return_tensors='pt')\n",
    "model = AutoModel.from_pretrained(MODEL)\n",
    "features = model(**encoded_input)\n",
    "features = features[0].detach().cpu().numpy() \n",
    "features_mean = np.mean(features[0], axis=0) \n",
    "\n",
    "\n",
    "features_mean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ac8A2XgzYpTk"
   },
   "source": [
    "## Masked language modeling\n",
    "\n",
    "Use Twitter-RoBERTA-base to predict words in context using the `fill-mask` pipeline in `transformers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-pCzp7DIYpTl",
    "outputId": "2dccfcf7-6fe8-492e-8943-cfe1b51d87fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "I am so <mask> 😊\n",
      "1)  happy 0.402\n",
      "2)  excited 0.1441\n",
      "3)  proud 0.143\n",
      "4)  grateful 0.0669\n",
      "5)  blessed 0.0334\n",
      "------------------------------\n",
      "I am so <mask> 😢\n",
      "1)  sad 0.2641\n",
      "2)  sorry 0.1605\n",
      "3)  tired 0.138\n",
      "4)  sick 0.0278\n",
      "5)  hungry 0.0232\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline, AutoTokenizer\n",
    "import numpy as np\n",
    "\n",
    "MODEL = \"cardiffnlp/twitter-roberta-base\"\n",
    "fill_mask = pipeline(\"fill-mask\", model=MODEL, tokenizer=MODEL)\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
    "\n",
    "def print_candidates():\n",
    "    for i in range(5):\n",
    "        token = tokenizer.decode(candidates[i]['token'])\n",
    "        score = np.round(candidates[i]['score'], 4)\n",
    "        print(f\"{i+1}) {token} {score}\")\n",
    "\n",
    "texts = [\n",
    " \"I am so <mask> 😊\",\n",
    " \"I am so <mask> 😢\" \n",
    "]\n",
    "for text in texts:\n",
    "    t = preprocess(text)\n",
    "    print(f\"{'-'*30}\\n{t}\")\n",
    "    candidates = fill_mask(t)\n",
    "    print_candidates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Is2p3X9iYpTq"
   },
   "source": [
    "## Use TweetEval Classifiers\n",
    "\n",
    "We currently provide the following fine-tuned models for different tweet classification tasks:\n",
    "\n",
    "- emoji prediction (`emoji`)\n",
    "- emotion detection (`emotion`)\n",
    "- hate speech detection (`hate`)\n",
    "- irony detection (`irony`)\n",
    "- offensive language identification (`offensive`)\n",
    "- sentiment analysis (`sentiment`)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "outputId": "858885f3-cc50-48ec-9b57-db533a98bb52"
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   "outputs": [],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "from transformers import TFAutoModelForSequenceClassification\n",
    "from transformers import AutoTokenizer\n",
    "import numpy as np\n",
    "from scipy.special import softmax\n",
    "import csv\n",
    "import urllib.request\n",
    "\n",
    "task='emoji'\n",
    "MODEL = f\"cardiffnlp/twitter-roberta-base-{task}\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "g8_fEezTYpTu"
   },
   "outputs": [],
   "source": [
    "# download label mapping\n",
    "mapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\n",
    "with urllib.request.urlopen(mapping_link) as f:\n",
    "    html = f.read().decode('utf-8').split(\"\\n\")\n",
    "    csvreader = csv.reader(html, delimiter='\\t')\n",
    "labels = [row[1] for row in csvreader if len(row) > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
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    },
    "id": "YQ_EGuYaYpTy",
    "outputId": "ed18d44d-b997-48d3-b894-a8d6482e3cad"
   },
   "outputs": [],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n",
    "\n",
    "text = \"Good night 😊\"\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mUG9EAWIYpT1",
    "outputId": "b59e5aa2-878a-4773-c676-3a9049a83307"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) 😘 0.2637\n",
      "2) ❤ 0.1952\n",
      "3) 💕 0.1171\n",
      "4) ✨ 0.0927\n",
      "5) 😊 0.0756\n",
      "6) 💜 0.046\n",
      "7) 💙 0.0444\n",
      "8) 😍 0.0272\n",
      "9) 😉 0.0228\n",
      "10) 😎 0.0198\n",
      "11) 😜 0.0166\n",
      "12) 😂 0.0132\n",
      "13) 😁 0.0131\n",
      "14) ☀ 0.0112\n",
      "15) 🎄 0.009\n",
      "16) 💯 0.009\n",
      "17) 🔥 0.008\n",
      "18) 📷 0.0057\n",
      "19) 🇺🇸 0.005\n",
      "20) 📸 0.0048\n"
     ]
    }
   ],
   "source": [
    "ranking = np.argsort(scores)\n",
    "ranking = ranking[::-1]\n",
    "for i in range(scores.shape[0]):\n",
    "    l = labels[ranking[i]]\n",
    "    s = scores[ranking[i]]\n",
    "    print(f\"{i+1}) {l} {np.round(float(s), 4)}\")"
   ]
  }
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