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{
"cells": [
{
"cell_type": "markdown",
"id": "750fed8c",
"metadata": {},
"source": [
"Must run the following:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ccad76ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D:\\Research\\FinancialMarkets\\Emotions\\Emtract\\Training\\EmTract\n"
]
}
],
"source": [
"!git clone https://github.com/dvamossy/EmTract.git\n",
"%cd EmTract\n",
"!pip install -r requirements.txt "
]
},
{
"cell_type": "markdown",
"id": "2551adee",
"metadata": {},
"source": [
"Text Cleaner for unprocessed text"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "687995ef",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Research\\FinancialMarkets\\Emotions\\Emtract\\Training\\EmTract\\emtract\\processors\\cleaning.py:68: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" symspell_list = pd.read_csv(\n"
]
},
{
"data": {
"text/plain": [
"'soo well'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from emtract.processors.cleaning import clean_text\n",
"# Illustrate text cleaning\n",
"clean_text(\"soooooo well\", segment_words=False)"
]
},
{
"cell_type": "markdown",
"id": "6b81c0cd",
"metadata": {},
"source": [
"Option I"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ca68eb1",
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"classifier = pipeline(\"text-classification\", model=\"vamossyd/emtract-distilbert-base-uncased-emotion\", return_all_scores=True)\n",
"classifier(\"i love this!\")"
]
},
{
"cell_type": "markdown",
"id": "0b9cd58f",
"metadata": {},
"source": [
"Option II"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "524cb5d6",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import pandas as pd\n",
"import numpy as np\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer\n",
"\n",
"# Create class for data preparation\n",
"class SimpleDataset:\n",
" def __init__(self, tokenized_texts):\n",
" self.tokenized_texts = tokenized_texts\n",
" \n",
" def __len__(self):\n",
" return len(self.tokenized_texts[\"input_ids\"])\n",
" \n",
" def __getitem__(self, idx):\n",
" return {k: v[idx] for k, v in self.tokenized_texts.items()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f9f01f4",
"metadata": {},
"outputs": [],
"source": [
"input_path = \"PROVIDE_PATH_TO_DATA\"\n",
"# data = pd.read_csv(input_path) # ASSUMING DATA IS IN CSV\n",
"\n",
"# If text is already cleaned:\n",
"# texts = data.text.tolist() \n",
"\n",
"# Otherwise:\n",
"# texts = data['text'].apply(clean_text).tolist() # \n",
"\n",
"# As an example:\n",
"texts = ['i love this', 'i do not love you', 'to the moon 🚀']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04ce5528",
"metadata": {},
"outputs": [],
"source": [
"# in case the model does not load, use git to clone it and use emtract-distilbert-base-uncased-emotion in the model_name field\n",
"\n",
"#!git clone https://huggingface.co/vamossyd/emtract-distilbert-base-uncased-emotion"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "839cd230",
"metadata": {},
"outputs": [],
"source": [
"# load tokenizer and model, create trainer\n",
"model_name = \"vamossyd/emtract-distilbert-base-uncased-emotion\"\n",
"# model_name = \"emtract-distilbert-base-uncased-emotion\" # in case the model does not load\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
"trainer = Trainer(model=model)\n",
"\n",
"# Tokenize texts and create prediction data set\n",
"tokenized_texts = tokenizer(texts, truncation=True, padding=True)\n",
"pred_dataset = SimpleDataset(tokenized_texts)\n",
"predictions = trainer.predict(pred_dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d903549",
"metadata": {},
"outputs": [],
"source": [
"# scores raw\n",
"temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True))\n",
"preds = predictions.predictions.argmax(-1)\n",
"labels = pd.Series(preds).map(model.config.id2label)\n",
"\n",
"# container\n",
"anger = []\n",
"disgust = []\n",
"fear = []\n",
"happy = []\n",
"neutral = []\n",
"sadness = []\n",
"surprise = []\n",
"\n",
"# extract scores (as many entries as exist in pred_texts)\n",
"for i in range(len(texts)):\n",
" anger.append(temp[i][3])\n",
" disgust.append(temp[i][4])\n",
" fear.append(temp[i][6])\n",
" happy.append(temp[i][1])\n",
" neutral.append(temp[i][0])\n",
" sadness.append(temp[i][2])\n",
" surprise.append(temp[i][5])\n",
" \n",
"df = pd.DataFrame(list(zip(texts, labels, anger, disgust, fear, happy, neutral, sadness, surprise)), columns=['text','pred_label', 'anger', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "577f10b8",
"metadata": {},
"outputs": [],
"source": [
"# save results to csv\n",
"output_path = \"YOUR_FILENAME_EMOTIONS.csv\" # name your output file\n",
"# df.to_csv(YOUR_FILENAME)"
]
},
{
"cell_type": "markdown",
"id": "ddd22317",
"metadata": {},
"source": [
"Option III\n",
"\n",
"Batch prediction in case data is too large."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f39375b",
"metadata": {},
"outputs": [],
"source": [
"# Specify batch size\n",
"batch_size = 100000\n",
"\n",
"# Split the texts into batches\n",
"text_batches = [texts[i:i + batch_size] for i in range(0, len(texts), batch_size)]\n",
"\n",
"# Store the predictions\n",
"all_predictions = []\n",
"\n",
"# Iterate through batches\n",
"for batch in tqdm(text_batches):\n",
" # Tokenize texts and create prediction dataset\n",
" tokenized_texts = tokenizer(batch, truncation=True, padding=True)\n",
" pred_dataset = SimpleDataset(tokenized_texts)\n",
" predictions = trainer.predict(pred_dataset)[0]\n",
" all_predictions.extend(predictions)\n",
"\n",
"all_predictions = np.array(all_predictions)\n",
"\n",
"# scores raw\n",
"temp = (np.exp(all_predictions)/np.exp(all_predictions).sum(-1,keepdims=True))\n",
"\n",
"# container\n",
"anger = []\n",
"disgust = []\n",
"fear = []\n",
"happy = []\n",
"neutral = []\n",
"sadness = []\n",
"surprise = []\n",
"\n",
"# extract scores (as many entries as exist in pred_texts)\n",
"for i in range(len(texts)):\n",
" anger.append(temp[i][3])\n",
" disgust.append(temp[i][4])\n",
" fear.append(temp[i][6])\n",
" happy.append(temp[i][1])\n",
" neutral.append(temp[i][0])\n",
" sadness.append(temp[i][2])\n",
" surprise.append(temp[i][5])\n",
" \n",
"df = pd.DataFrame(list(zip(texts, anger, disgust, fear, happy, neutral, sadness, surprise)), columns=['text', 'anger', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'])\n",
"df.head()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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