adding statistics

README.md

@@ -4,7 +4,7 @@
 
 Inspec is a dataset for benchmarking keyphrase extraction and generation models.
 The dataset is composed of 2,000 abstracts of scientific papers collected from the [Inspec database](https://www.theiet.org/resources/inspec/).
-Keyphrases were annotated by professional indexers in an uncontrolled setting, that is, they are not limited to entries in a thesaurus.
+Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
 Details about the inspec dataset can be found in the original paper:
  - Anette Hulth. 2003.
    [Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028). 
@@ -19,11 +19,11 @@ Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>
 
 The dataset is divided into the following three splits:
 
-| Split      | # documents |
-| :--------- |-----------: |
-| Train      | 1,000       |
-| Test       | 500         | 
-| Validation | 500         | 
+| Split      | # documents | # keyphrases | % Present | % Mixed | % Reordered | % Unseen |
+| :--------- | ----------: | -----------: | --------: | ------: | ----------: | -------: |
+| Train      | 1,000       | 9.79         | 77.83     | 9.90    | 6.30        | 5.98     |
+| Test       | 500         | 9.15         | 77.90     | 9.82    | 6.74        | 5.54     |
+| Validation | 500         | 9.83         | 78.49     | 9.82    | 6.76        | 4.92     |
 
 The following data fields are available :
 

stats.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eba2ee81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No config specified, defaulting to: inspec/raw\n",
+      "Reusing dataset inspec (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___inspec/raw/1.1.0/156f7172f117a205a845b4378fa15df490952487673e2d2fff33b39c1173b661)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ee1af5876804725adcd149763bd27b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset('taln-ls2n/inspec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4ba72244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a14738b9d72b45d29d24cd764e272fd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "statistics for train\n",
+      "# keyphrases: 9.79\n",
+      "% P: 77.83\n",
+      "% R: 9.90\n",
+      "% M: 6.30\n",
+      "% U: 5.98\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59a4352f94d84ada80beb34607d63425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "statistics for validation\n",
+      "# keyphrases: 9.15\n",
+      "% P: 77.90\n",
+      "% R: 9.82\n",
+      "% M: 6.74\n",
+      "% U: 5.54\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77346b747c5248fb9566d8665c7017bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "statistics for test\n",
+      "# keyphrases: 9.83\n",
+      "% P: 78.49\n",
+      "% R: 9.82\n",
+      "% M: 6.76\n",
+      "% U: 4.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "\n",
+    "\n",
+    "for split in ['train', 'validation', 'test']:\n",
+    "    \n",
+    "    P, R, M, U, nb_kps = [], [], [], [], []\n",
+    "    \n",
+    "    for sample in tqdm(dataset[split]):\n",
+    "        nb_kps.append(len(sample[\"keyphrases\"]))\n",
+    "        P.append(sample[\"prmu\"].count(\"P\") / nb_kps[-1])\n",
+    "        R.append(sample[\"prmu\"].count(\"R\") / nb_kps[-1])\n",
+    "        M.append(sample[\"prmu\"].count(\"M\") / nb_kps[-1])\n",
+    "        U.append(sample[\"prmu\"].count(\"U\") / nb_kps[-1])\n",
+    "        \n",
+    "    print(\"statistics for {}\".format(split))\n",
+    "    print(\"# keyphrases: {:.2f}\".format(sum(nb_kps)/len(nb_kps)))\n",
+    "    print(\"% P: {:.2f}\".format(sum(P)/len(P)*100))\n",
+    "    print(\"% R: {:.2f}\".format(sum(R)/len(R)*100))\n",
+    "    print(\"% M: {:.2f}\".format(sum(M)/len(M)*100))\n",
+    "    print(\"% U: {:.2f}\".format(sum(U)/len(U)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e4dcdab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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+  },
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+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}