Adding stats

.gitignore

@@ -2,3 +2,4 @@
 .idea/
 src/
 **.DS_Store
+.ipynb_checkpoints/

README.md

@@ -57,8 +57,8 @@ The dataset is divided into the following two splits:
 
 | Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
 | :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:|
-| Train      |         144 |      - |            - |         - |           - |       - |        - |
-| Test       |         100 |      - |            - |         - |           - |       - |        - |
+| Train      |         144 |  184.6 |        15.44 |     42.16 |        7.36 |   26.85 |    23.63 |
+| Test       |         100 |  203.1 |        14.66 |     40.11 |        8.34 |   27.12 |    24.43 |
 
 Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers.
 

stats.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eba2ee81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No config specified, defaulting to: sem_eval/raw\n",
+      "Reusing dataset sem_eval (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___sem_eval/raw/1.0.0/b40e008b5c96137733e24d9d244d70aa1fe6353ee65e180d8f6948af4027fbe4)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9379b6f5f5d1483ab184db7486ac67b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset('taln-ls2n/semeval-2010-pre')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ba72244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c14c3725089d4b5284e36df4cf90d3da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# keyphrases: 14.66\n",
+      "% P: 40.11\n",
+      "% R: 8.34\n",
+      "% M: 27.12\n",
+      "% U: 24.43\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "P, R, M, U, nb_kps = [], [], [], [], []\n",
+    "    \n",
+    "for sample in tqdm(dataset['test']):\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(\"# 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": 3,
+   "id": "52dda817",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy\n",
+    "\n",
+    "nlp = spacy.load(\"en_core_web_sm\")\n",
+    "\n",
+    "# https://spacy.io/usage/linguistic-features#native-tokenizer-additions\n",
+    "\n",
+    "from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER\n",
+    "from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS\n",
+    "from spacy.util import compile_infix_regex\n",
+    "\n",
+    "# Modify tokenizer infix patterns\n",
+    "infixes = (\n",
+    "    LIST_ELLIPSES\n",
+    "    + LIST_ICONS\n",
+    "    + [\n",
+    "        r\"(?<=[0-9])[+\\-\\*^](?=[0-9-])\",\n",
+    "        r\"(?<=[{al}{q}])\\.(?=[{au}{q}])\".format(\n",
+    "            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES\n",
+    "        ),\n",
+    "        r\"(?<=[{a}]),(?=[{a}])\".format(a=ALPHA),\n",
+    "        # ✅ Commented out regex that splits on hyphens between letters:\n",
+    "        # r\"(?<=[{a}])(?:{h})(?=[{a}])\".format(a=ALPHA, h=HYPHENS),\n",
+    "        r\"(?<=[{a}0-9])[:<>=/](?=[{a}])\".format(a=ALPHA),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "infix_re = compile_infix_regex(infixes)\n",
+    "nlp.tokenizer.infix_finditer = infix_re.finditer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "047ab1cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "209e7faf7c454aeabc936c07919ac1fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "avg doc len: 203.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "doc_len = []\n",
+    "for sample in tqdm(dataset['test']):\n",
+    "    doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
+    "        \n",
+    "print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len)))        "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}