diff --git a/README.md b/README.md index 6347b93..07273b5 100644 --- a/README.md +++ b/README.md @@ -12,11 +12,10 @@ Perplexity hashed by Zadania ----------------- 1. Statystyczny model językowy (zadanie 5) - - branch: master - Perplexity hashed on `dev-0`: 555.75 - - branch: 05_ngram - Perplexity hashed on `dev-0`: xxx + - branch: master - Perplexity hashed on `dev-0`: 549.12

2. Neuronowy model językowy (zadanie 7) - - branch: 07_neural - Perplexity hashed on `dev-0`: xxx + - branch: 07_neural - Perplexity hashed on `dev-0`: 465.53

3. Model neuronowy rekurencyjny (zadanie 9) - branch: 09_neural - Perplexity hashed on `dev-0`: xxx diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 35efc67..c393837 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,29 +1,29 @@ :1 -passage:0.08 growth:0.07 successors:0.07 place,:0.06 use,:0.05 head:0.05 own:0.05 own,:0.04 power:0.04 place:0.04 functions,:0.04 members:0.04 work:0.04 value:0.04 principles:0.03 strength:0.03 beauty:0.03 action,:0.03 history:0.03 value,:0.03 :0.11 -a:0.57 the:0.28 lha:0.14 :0.01 +successors:0.08 passage:0.07 place,:0.07 growth:0.06 use,:0.05 head:0.05 functions,:0.04 power:0.04 place:0.04 own,:0.04 own:0.04 members:0.04 work:0.04 principles:0.04 strength:0.04 value:0.04 beauty:0.03 business:0.03 size:0.03 history:0.03 :0.1 +a:0.5 lha:0.25 the:0.25 :0.01 :1 :1 -a:0.43 him:0.1 two:0.08 means:0.04 just:0.03 only:0.03 money:0.03 good\nand:0.02 all:0.02 force:0.02 the:0.01 capital:0.01 no:0.01 barely:0.01 capacity:0.01 bills:0.01 scarcely:0.01 boats:0.01 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ed:0.03 or:0.02 lies:0.02 as:0.01 centers:0.01 :0.1 :1 :1 \ No newline at end of file diff --git a/main.ipynb b/main.ipynb index 5ad5c56..99d48f4 100644 --- a/main.ipynb +++ b/main.ipynb @@ -2,16 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import csv\n", - "import os\n", "import re\n", - "import random\n", "from collections import Counter, defaultdict\n", "import nltk\n", "import math\n", @@ -20,11 +18,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "directory = \"train/in.tsv.xz\"\n", + "directory_expected = \"train/expected.tsv\"\n", "directory_dev_0 = \"dev-0/in.tsv.xz\"\n", "directory_test_A = \"test-A/in.tsv.xz\"" ] @@ -38,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -121,13 +120,17 @@ "source": [ "dataframeList = pd.read_csv(directory, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, chunksize=10000)\n", "\n", - "expectedList = pd.read_csv(directory, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000)\n", + "expectedList = pd.read_csv(directory_expected, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000)\n", "\n", "DATASET = \"\"\n", "\n", "for number, (dataframe, expected) in enumerate(zip(dataframeList, expectedList)):\n", + " dataframe = dataframe.reset_index()\n", " dataframe = dataframe.replace(r'\\\\r|\\\\n|\\n|\\\\t', ' ', regex=True)\n", "\n", + " expected['Word'] = expected['Word'].apply(lambda x: str(x).strip())\n", + " word = expected['Word']\n", + "\n", " left_text = dataframe['LeftContext'].to_list()\n", " right_text = dataframe['RightContext'].to_list()\n", " word = expected['Word'].to_list()\n", @@ -149,14 +152,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 199099663/199099663 [11:00<00:00, 301572.30it/s] \n" + "100%|██████████| 139475976/139475976 [04:39<00:00, 498903.89it/s]\n" ] } ], @@ -167,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -206,21 +209,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "10519it [00:51, 206.24it/s]" + "10519it [00:27, 385.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": 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0.039034540158135664), ('from', 0.03603828547648772), ('with', 0.025551394090719933), ('up', 0.015064502704952144), ('upon', 0.014565126924677487), ('over', 0.013816063254265501), ('into', 0.013316687473990845), ('and', 0.012900540990428632), ('by', 0.012068248023304202), ('through', 0.011652101539741989), ('In', 0.011319184352892218), ('all', 0.010486891385767791), ('out', 0.010320432792342904), ('down', 0.00807324178110695)], []]\n" ] }, { @@ -308,14 +311,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 7414/7414 [00:00<00:00, 114060.60it/s]\n" + "100%|██████████| 7414/7414 [00:00<00:00, 112933.06it/s]\n" ] } ], @@ -325,11 +328,18 @@ " file.write(result)\n", " file.close()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "python11", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -344,9 +354,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/run.py b/run.py index e65dc2f..5bc3347 100644 --- a/run.py +++ b/run.py @@ -1,15 +1,14 @@ import pandas as pd import numpy as np import csv -import os import re -import random from collections import Counter, defaultdict import nltk import math from tqdm import tqdm directory = "train/in.tsv.xz" +directory_expected = "train/expected.tsv" directory_dev_0 = "dev-0/in.tsv.xz" directory_test_A = "test-A/in.tsv.xz" @@ -70,13 +69,17 @@ class Model(): dataframeList = pd.read_csv(directory, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, chunksize=10000) -expectedList = pd.read_csv(directory, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000) +expectedList = pd.read_csv(directory_expected, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000) DATASET = "" for number, (dataframe, expected) in enumerate(zip(dataframeList, expectedList)): + dataframe = dataframe.reset_index() dataframe = dataframe.replace(r'\\r|\\n|\n|\\t', ' ', regex=True) + expected['Word'] = expected['Word'].apply(lambda x: str(x).strip()) + word = expected['Word'] + left_text = dataframe['LeftContext'].to_list() right_text = dataframe['RightContext'].to_list() word = expected['Word'].to_list() diff --git a/test-A/out.tsv b/test-A/out.tsv index beabb8f..1a7f5af 100644 --- a/test-A/out.tsv +++ b/test-A/out.tsv @@ -1,89 +1,89 @@ :1 :1 -the:0.9 tho:0.09 :0.01 -man:0.16 plan:0.08 trial:0.06 living:0.06 statement:0.05 law:0.05 class:0.05 time:0.04 year:0.04 vote:0.03 government:0.03 single:0.03 day:0.03 question:0.03 sensation:0.03 bill:0.03 little:0.03 corporation,:0.02 way:0.02 means:0.02 :0.11 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