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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
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-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*.pcm filter=lfs diff=lfs merge=lfs -text
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-# Audio files - compressed
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README.md

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-# SentEval Customer Reviews
-
-This dataset is a port of the official [SentEval `CR` dataset](https://nlp.stanford.edu/~sidaw/home/projects:nbsvm) from [this paper](https://dl.acm.org/doi/10.1145/1014052.1014073). The test split was created from the by randomly sampling 20% of the original data and the train split is the remaining 80%. there are no official train/test splits of CR.
-There is no validation split. This was used in the STraTA paper.

SetFit--SentEval-CR/json-test.parquet

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SetFit--SentEval-CR/json-train.parquet

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create_dataset.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import glob"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = 'custrev.*'\n",
-    "text, label_text, label = [], [], []\n",
-    "for file in glob.glob(path):\n",
-    "    with open(file, 'r') as f:\n",
-    "        cr, sentiment = file.split('.')\n",
-    "        \n",
-    "        if sentiment == 'pos':\n",
-    "            lab_text = 'positive'\n",
-    "            lab = 1\n",
-    "\n",
-    "        else:\n",
-    "            lab_text = 'negative'\n",
-    "            lab = 0\n",
-    "\n",
-    "        \n",
-    "        for line in f:\n",
-    "            if line.strip():\n",
-    "                text.append(line.rstrip())\n",
-    "                label_text.append(lab_text)\n",
-    "                label.append(lab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame({'text':text, 'label':label, 'label_text':label_text}).drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = df.sample(frac=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train = df.drop(test.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('test.jsonl', 'w') as f:\n",
-    "    f.write(test.to_json(orient='records', lines=True))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('train.jsonl', 'w') as f:\n",
-    "    f.write(train.to_json(orient='records', lines=True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('base')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "f4f7bf0ead705f9496960dd44ed8785939deb14cc456821001bcb47e882c9346"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}