Upload script to generate the dataset

notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset, DatasetDict, concatenate_datasets\n",
+    "\n",
+    "columns = set(['premise', 'hypothesis', 'subset', 'label_name'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SNLI\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snli = load_dataset('snli')\n",
+    "snli = DatasetDict({'train': snli['train'], 'test': snli['validation']})\n",
+    "snli"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {0: 'entailment', 1: 'non-entailment', 2: 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['label']]\n",
+    "    sample['subset'] = 'snli'\n",
+    "    return sample\n",
+    "\n",
+    "snli = snli.filter(lambda x: x['label'] != -1, batched=False)\n",
+    "snli = snli.map(label_name, batched=False, remove_columns=list(set(snli['train'].column_names) - columns))\n",
+    "snli"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MNLI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnli = load_dataset('multi_nli')\n",
+    "mnli = DatasetDict({'train': mnli['train'], 'test': mnli['validation_matched']})\n",
+    "mnli"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {0: 'entailment', 1: 'non-entailment', 2: 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['label']]\n",
+    "    sample['subset'] = 'mnli'\n",
+    "    return sample\n",
+    "\n",
+    "mnli = mnli.map(label_name, batched=False, remove_columns=list(set(mnli['train'].column_names) - columns))\n",
+    "mnli"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FEVER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fever = load_dataset('pietrolesci/nli_fever')\n",
+    "fever = DatasetDict({'train': fever['train'], 'test': fever['dev']})\n",
+    "fever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {'SUPPORTS': 'entailment', 'REFUTES': 'non-entailment', 'NOT ENOUGH INFO': 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['fever_gold_label']]\n",
+    "    sample['subset'] = 'fever'\n",
+    "    return sample\n",
+    "\n",
+    "fever = fever.map(label_name, batched=False, remove_columns=list(set(fever['train'].column_names) - columns))\n",
+    "fever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SciTail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scitail = load_dataset('scitail', 'snli_format')\n",
+    "scitail = DatasetDict({'train': scitail['train'], 'test': scitail['validation']})\n",
+    "scitail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {'entailment': 'entailment', 'neutral': 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['gold_label']]\n",
+    "    sample['premise'] = sample['sentence1']\n",
+    "    sample['hypothesis'] = sample['sentence2']\n",
+    "    sample['subset'] = 'scitail'\n",
+    "    return sample\n",
+    "\n",
+    "scitail = scitail.map(label_name, batched=False, remove_columns=list(set(scitail['train'].column_names) - columns))\n",
+    "scitail"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PAWS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paws = load_dataset('paws', 'labeled_final')\n",
+    "paws = DatasetDict({'train': paws['train'], 'test': paws['validation']})\n",
+    "paws"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {1: 'entailment', 0: 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['label']]\n",
+    "    sample['premise'] = sample['sentence1']\n",
+    "    sample['hypothesis'] = sample['sentence2']\n",
+    "    sample['subset'] = 'paws'\n",
+    "    return sample\n",
+    "\n",
+    "paws = paws.map(label_name, batched=False, remove_columns=list(set(paws['train'].column_names) - columns))\n",
+    "paws"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# VitaminC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vitaminc = load_dataset('tals/vitaminc')\n",
+    "vitaminc = DatasetDict({'train': vitaminc['train'], 'test': vitaminc['validation']})\n",
+    "vitaminc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {'SUPPORTS': 'entailment', 'REFUTES': 'non-entailment', 'NOT ENOUGH INFO': 'non-entailment'}\n",
+    "\n",
+    "def label_name(sample):\n",
+    "    sample['label_name'] = mapping[sample['label']]\n",
+    "    sample['premise'] = sample['evidence']\n",
+    "    sample['hypothesis'] = sample['claim']\n",
+    "    sample['subset'] = 'vitaminc'\n",
+    "    return sample\n",
+    "\n",
+    "vitaminc = vitaminc.map(label_name, batched=False, remove_columns=list(set(vitaminc['train'].column_names) - columns))\n",
+    "vitaminc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NLI Mixture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nli_train = concatenate_datasets([snli['train'], mnli['train'], fever['train'], scitail['train'], paws['train'], vitaminc['train']])\n",
+    "nli_test = concatenate_datasets([snli['test'], mnli['test'], fever['test'], scitail['test'], paws['test'], vitaminc['test']])\n",
+    "\n",
+    "nli_dataset = DatasetDict({'train': nli_train, 'test': nli_test})\n",
+    "nli_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nli_dataset.push_to_hub('AntoineBlanot/nli_mixture')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp-train",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}