From 9cfce11e78a38c6a39761ef3dfb04ff7012936ba Mon Sep 17 00:00:00 2001
From: Ludovic Moncla <moncla.ludovic@gmail.com>
Date: Mon, 16 Jan 2023 15:02:16 +0100
Subject: [PATCH] Update Classification_Zero-Shot-Learning.ipynb
---
.../Classification_Zero-Shot-Learning.ipynb | 469 ++----------------
1 file changed, 29 insertions(+), 440 deletions(-)
diff --git a/notebooks/Classification_Zero-Shot-Learning.ipynb b/notebooks/Classification_Zero-Shot-Learning.ipynb
index ad73e6a..2165378 100644
--- a/notebooks/Classification_Zero-Shot-Learning.ipynb
+++ b/notebooks/Classification_Zero-Shot-Learning.ipynb
@@ -95,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"id": "bcptSr6o3ac7"
},
@@ -103,7 +103,7 @@
"source": [
"import pandas as pd\n",
"from tqdm import tqdm\n",
- "from transformers import BartForSequenceClassification, BartTokenizer\n"
+ "from transformers import BartForSequenceClassification, BartTokenizer"
]
},
{
@@ -143,7 +143,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -158,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -166,170 +166,7 @@
"id": "LRKJzWmf3pCg",
"outputId": "686c3ef4-8267-4266-95af-7193725aadca"
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>volume</th>\n",
- " <th>numero</th>\n",
- " <th>head</th>\n",
- " <th>normClass</th>\n",
- " <th>classEDdA</th>\n",
- " <th>author</th>\n",
- " <th>id_enccre</th>\n",
- " <th>domaine_enccre</th>\n",
- " <th>ensemble_domaine_enccre</th>\n",
- " <th>content</th>\n",
- " <th>contentWithoutClass</th>\n",
- " <th>firstParagraph</th>\n",
- " <th>nb_word</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>11</td>\n",
- " <td>2973</td>\n",
- " <td>ORNIS</td>\n",
- " <td>Commerce</td>\n",
- " <td>Comm.</td>\n",
- " <td>unsigned</td>\n",
- " <td>v11-1767-0</td>\n",
- " <td>commerce</td>\n",
- " <td>Commerce</td>\n",
- " <td>ORNIS, s. m. toile des Indes, (Comm.) sortes d...</td>\n",
- " <td>ORNIS, s. m. toile des Indes, () sortes de\\nto...</td>\n",
- " <td>ORNIS, s. m. toile des Indes, () sortes de\\nto...</td>\n",
- " <td>45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3</td>\n",
- " <td>3525</td>\n",
- " <td>COMPRENDRE</td>\n",
- " <td>Philosophie</td>\n",
- " <td>terme de Philosophie,</td>\n",
- " <td>Diderot</td>\n",
- " <td>v3-1722-0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>* COMPRENDRE, v. act. terme de Philosophie,\\nc...</td>\n",
- " <td>* COMPRENDRE, v. act. \\nc'est appercevoir la l...</td>\n",
- " <td>* COMPRENDRE, v. act. \\nc'est appercevoir la l...</td>\n",
- " <td>92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>2560</td>\n",
- " <td>ANCRE</td>\n",
- " <td>Marine</td>\n",
- " <td>Marine</td>\n",
- " <td>d'Alembert & Diderot</td>\n",
- " <td>v1-1865-0</td>\n",
- " <td>marine</td>\n",
- " <td>Marine</td>\n",
- " <td>ANCRE, s. f. (Marine.) est un instrument de fe...</td>\n",
- " <td>ANCRE, s. f. (.) est un instrument de fer\\nABC...</td>\n",
- " <td>ANCRE, s. f. (.) est un instrument de fer\\nABC...</td>\n",
- " <td>3327</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>16</td>\n",
- " <td>4241</td>\n",
- " <td>VAKEBARO</td>\n",
- " <td>Géographie moderne</td>\n",
- " <td>Géog. mod.</td>\n",
- " <td>unsigned</td>\n",
- " <td>v16-2587-0</td>\n",
- " <td>géographie</td>\n",
- " <td>Géographie</td>\n",
- " <td>VAKEBARO, (Géog. mod.) vallée du royaume\\nd'Es...</td>\n",
- " <td>VAKEBARO, () vallée du royaume\\nd'Espagne dans...</td>\n",
- " <td>VAKEBARO, () vallée du royaume\\nd'Espagne dans...</td>\n",
- " <td>34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>8</td>\n",
- " <td>3281</td>\n",
- " <td>INSPECTEUR</td>\n",
- " <td>Histoire ancienne</td>\n",
- " <td>Hist. anc.</td>\n",
- " <td>unsigned</td>\n",
- " <td>v8-2533-0</td>\n",
- " <td>histoire</td>\n",
- " <td>Histoire</td>\n",
- " <td>INSPECTEUR, s. m. inspector ; (Hist. anc.) cel...</td>\n",
- " <td>INSPECTEUR, s. m. inspector ; () celui \\nà qui...</td>\n",
- " <td>INSPECTEUR, s. m. inspector ; () celui \\nà qui...</td>\n",
- " <td>102</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " volume numero head normClass classEDdA \\\n",
- "0 11 2973 ORNIS Commerce Comm. \n",
- "1 3 3525 COMPRENDRE Philosophie terme de Philosophie, \n",
- "2 1 2560 ANCRE Marine Marine \n",
- "3 16 4241 VAKEBARO Géographie moderne Géog. mod. \n",
- "4 8 3281 INSPECTEUR Histoire ancienne Hist. anc. \n",
- "\n",
- " author id_enccre domaine_enccre ensemble_domaine_enccre \\\n",
- "0 unsigned v11-1767-0 commerce Commerce \n",
- "1 Diderot v3-1722-0 NaN NaN \n",
- "2 d'Alembert & Diderot v1-1865-0 marine Marine \n",
- "3 unsigned v16-2587-0 géographie Géographie \n",
- "4 unsigned v8-2533-0 histoire Histoire \n",
- "\n",
- " content \\\n",
- "0 ORNIS, s. m. toile des Indes, (Comm.) sortes d... \n",
- "1 * COMPRENDRE, v. act. terme de Philosophie,\\nc... \n",
- "2 ANCRE, s. f. (Marine.) est un instrument de fe... \n",
- "3 VAKEBARO, (Géog. mod.) vallée du royaume\\nd'Es... \n",
- "4 INSPECTEUR, s. m. inspector ; (Hist. anc.) cel... \n",
- "\n",
- " contentWithoutClass \\\n",
- "0 ORNIS, s. m. toile des Indes, () sortes de\\nto... \n",
- "1 * COMPRENDRE, v. act. \\nc'est appercevoir la l... \n",
- "2 ANCRE, s. f. (.) est un instrument de fer\\nABC... \n",
- "3 VAKEBARO, () vallée du royaume\\nd'Espagne dans... \n",
- "4 INSPECTEUR, s. m. inspector ; () celui \\nà qui... \n",
- "\n",
- " firstParagraph nb_word \n",
- "0 ORNIS, s. m. toile des Indes, () sortes de\\nto... 45 \n",
- "1 * COMPRENDRE, v. act. \\nc'est appercevoir la l... 92 \n",
- "2 ANCRE, s. f. (.) est un instrument de fer\\nABC... 3327 \n",
- "3 VAKEBARO, () vallée du royaume\\nd'Espagne dans... 34 \n",
- "4 INSPECTEUR, s. m. inspector ; () celui \\nà qui... 102 "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df = pd.read_csv(input_path + test_set_path, sep=\"\\t\")\n",
"df.head()"
@@ -337,27 +174,16 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(15854, 13)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -368,7 +194,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -377,77 +203,18 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(13441, 13)"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Commerce',\n",
- " 'Marine',\n",
- " 'Géographie',\n",
- " 'Histoire',\n",
- " 'Belles-lettres - Poésie',\n",
- " 'Economie domestique',\n",
- " 'Droit - Jurisprudence',\n",
- " 'Médecine - Chirurgie',\n",
- " 'Militaire (Art) - Guerre - Arme',\n",
- " 'Beaux-arts',\n",
- " 'Antiquité',\n",
- " 'Histoire naturelle',\n",
- " 'Grammaire',\n",
- " 'Philosophie',\n",
- " 'Arts et métiers',\n",
- " 'Pharmacie',\n",
- " 'Religion',\n",
- " 'Pêche',\n",
- " 'Anatomie',\n",
- " 'Architecture',\n",
- " 'Musique',\n",
- " 'Jeu',\n",
- " 'Caractères',\n",
- " 'Métiers',\n",
- " 'Physique - [Sciences physico-mathématiques]',\n",
- " 'Maréchage - Manège',\n",
- " 'Chimie',\n",
- " 'Blason',\n",
- " 'Chasse',\n",
- " 'Mathématiques',\n",
- " 'Médailles',\n",
- " 'Superstition',\n",
- " 'Agriculture - Economie rustique',\n",
- " 'Mesure',\n",
- " 'Monnaie',\n",
- " 'Minéralogie',\n",
- " 'Politique',\n",
- " 'Spectacle']"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"classes = df[column_class].unique().tolist()\n",
"classes"
@@ -471,80 +238,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cf58eccecc1847e48d520a83040e3ec7",
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- "version_minor": 0
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- "Downloading: 0%| | 0.00/899k [00:00<?, ?B/s]"
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- ]
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- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# load model pretrained on MNLI\n",
"tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-mnli')\n",
@@ -583,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -638,84 +334,18 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\"ORNIS, s. m. toile des Indes, (Comm.) sortes de\\ntoiles de coton ou de mousseline, qui se font a Brampour ville de l'Indoustan, entre Surate & Agra. Ces\\ntoiles sont par bandes, moitié coton & moitié or &\\nargent. Il y en a depuis quinze jusqu'à vingt aunes.\""
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df[column_text].tolist()[0]"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The hypothesis with the highest score is: \"Commerce\" with a probability of 70.05%\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[('Commerce', 70.05096077919006),\n",
- " ('Anatomie', 68.73840689659119),\n",
- " ('Politique', 60.71174740791321),\n",
- " ('Géographie', 59.156250953674316),\n",
- " ('Architecture', 58.74174237251282),\n",
- " ('Histoire', 57.459235191345215),\n",
- " ('Agriculture - Economie rustique', 53.53081226348877),\n",
- " ('Histoire naturelle', 48.459288477897644),\n",
- " ('Antiquité', 46.68458700180054),\n",
- " ('Beaux-arts', 42.856183648109436),\n",
- " ('Mesure', 41.31035804748535),\n",
- " ('Jeu', 41.22118949890137),\n",
- " ('Droit - Jurisprudence', 41.1332905292511),\n",
- " ('Minéralogie', 38.137245178222656),\n",
- " ('Spectacle', 37.80339956283569),\n",
- " ('Pêche', 37.214648723602295),\n",
- " ('Superstition', 36.727988719940186),\n",
- " ('Arts et métiers', 36.511969566345215),\n",
- " ('Métiers', 36.5054726600647),\n",
- " ('Monnaie', 35.89862287044525),\n",
- " ('Musique', 32.74966776371002),\n",
- " ('Mathématiques', 32.70111680030823),\n",
- " ('Chasse', 29.35197949409485),\n",
- " ('Economie domestique', 28.346234560012817),\n",
- " ('Philosophie', 27.653270959854126),\n",
- " ('Chimie', 25.783824920654297),\n",
- " ('Physique - [Sciences physico-mathématiques]', 25.4037082195282),\n",
- " ('Médailles', 24.58679974079132),\n",
- " ('Grammaire', 22.36253321170807),\n",
- " ('Caractères', 20.14845609664917),\n",
- " ('Pharmacie', 19.720394909381866),\n",
- " ('Militaire (Art) - Guerre - Arme', 19.682711362838745),\n",
- " ('Médecine - Chirurgie', 18.615825474262238),\n",
- " ('Marine', 18.208028376102448),\n",
- " ('Belles-lettres - Poésie', 13.306896388530731),\n",
- " ('Blason', 10.476677119731903),\n",
- " ('Religion', 9.702161699533463),\n",
- " ('Maréchage - Manège', 4.211411997675896)]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"premise = df[column_text].tolist()[0]\n",
"\n",
@@ -732,20 +362,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Commerce'"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"probs[0][0]"
]
@@ -778,48 +397,18 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "672.05"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"len(texts) / 20"
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 1%| | 5/673 [10:28<23:19:26, 125.70s/it]\n"
- ]
- },
- {
- "ename": "IndexError",
- "evalue": "string index out of range",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[36], line 12\u001b[0m\n\u001b[1;32m 8\u001b[0m prob_labels \u001b[39m=\u001b[39m []\n\u001b[1;32m 10\u001b[0m \u001b[39mfor\u001b[39;00m j, content \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(batch):\n\u001b[0;32m---> 12\u001b[0m true_probs \u001b[39m=\u001b[39m zero_shot_prediction(content[i][:\u001b[39m1024\u001b[39m], classes)\n\u001b[1;32m 14\u001b[0m \u001b[39m#pred_labels.append(get_highest_score(true_probs, classes)[0])\u001b[39;00m\n\u001b[1;32m 15\u001b[0m prob_labels\u001b[39m.\u001b[39mappend(get_sorted_scores(true_probs, classes))\n",
- "\u001b[0;31mIndexError\u001b[0m: string index out of range"
- ]
- }
- ],
+ "outputs": [],
"source": [
"texts = df[column_text].tolist()\n",
"batch_size = 20\n",
@@ -830,9 +419,9 @@
"\n",
" prob_labels = []\n",
"\n",
- " for j, content in enumerate(batch):\n",
+ " for content in batch:\n",
"\n",
- " true_probs = zero_shot_prediction(content[j][:1024], classes)\n",
+ " true_probs = zero_shot_prediction(content[:512], classes)\n",
" \n",
" #pred_labels.append(get_highest_score(true_probs, classes)[0])\n",
" prob_labels.append(get_sorted_scores(true_probs, classes))\n",
@@ -844,7 +433,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": []
--
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