Ran files
This commit is contained in:
Henry Dowd
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "d2aa2997",
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"metadata": {},
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"outputs": [
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@@ -40,7 +40,7 @@
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" \n",
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" # Edge cases\n",
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" (\"Short.\", \"Short.\"), # Very short\n",
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" (\"A B C D E F G\", \"A B C D E F G\"), # Repeated words\n",
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" (\"A B C D E F G\", \"A B C D E F G\"), # Repeated words\n",
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" (\"\", \"\"), # Empty strings\n",
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"]\n",
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"print(\"done\")"
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@@ -56,7 +56,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 3,
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"id": "e60d024e969254a",
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"metadata": {
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"ExecuteTime": {
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@@ -121,7 +121,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 4,
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"id": "0b68fdcd",
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"metadata": {},
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"outputs": [
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@@ -130,18 +130,19 @@
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"output_type": "stream",
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"text": [
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"Sentence 1 Sentence 2: Similarities -> Char: Word:\n",
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"====================================================================================================\n"
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]
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},
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{
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"ename": "NameError",
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"evalue": "name 'test_pairs' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 31\u001b[39m\n\u001b[32m 28\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33mSentence 1\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m<41\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33mSentence 2:\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m<20\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33mSimilarities ->\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m<19\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33mChar:\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m<10\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33mWord:\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 29\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m * \u001b[32m100\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sent1, sent2 \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtest_pairs\u001b[49m:\n\u001b[32m 32\u001b[39m char_similarity = char_levenshtein_similarity(sent1, sent2)\n\u001b[32m 33\u001b[39m word_similarity = word_levenshtein_similarity(sent1, sent2)\n",
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"\u001b[31mNameError\u001b[39m: name 'test_pairs' is not defined"
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"====================================================================================================\n",
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"The cat sat on the mat. vs The cat sat on the mat. 1.000 1.000 \n",
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"The cat sat on the mat. vs The cat sat on the mat 0.957 0.833 \n",
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"The cat sat on the mat. vs The cat sat on the mat. 0.821 1.000 \n",
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"The cat sat on the mat. vs On the mat, the cat was sitting. 0.344 0.143 \n",
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"The cat sat on the mat. vs The feline rested on the rug. 0.517 0.500 \n",
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"The quick brown fox jumps. vs A fast brown fox leaps. 0.577 0.400 \n",
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"The cat sat on the mat. vs The dog ran in the park. 0.625 0.333 \n",
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"I love programming. vs She enjoys reading books. 0.200 0.000 \n",
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"The weather is nice today. vs It's raining outside. 0.192 0.000 \n",
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"Short. vs Short. 1.000 1.000 \n",
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"A B C D E F G vs A B C D E F G 1.000 1.000 \n",
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" vs 1.000 1.000 \n"
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]
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}
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],
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@@ -193,7 +194,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": null,
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"id": "9e20739a",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "46a985b4",
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"metadata": {},
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"outputs": [
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@@ -268,7 +277,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"id": "7dc7ac2e",
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"metadata": {},
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"outputs": [
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@@ -346,7 +355,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 7,
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"id": "e6a4d4e2",
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"metadata": {},
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"outputs": [
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@@ -415,7 +424,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "493979a4",
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"metadata": {},
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"outputs": [],
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@@ -444,7 +453,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 9,
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"id": "b3d07562",
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"metadata": {},
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"outputs": [
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@@ -665,7 +674,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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"version": "3.13.12"
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}
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},
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"nbformat": 4,
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