init solution
This commit is contained in:
Łukasz Jędyk
parent
2c27ab71da
commit
3dd8f4130a
6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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@ -0,0 +1,6 @@
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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238
.ipynb_checkpoints/run-checkpoint.ipynb
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238
.ipynb_checkpoints/run-checkpoint.ipynb
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@ -0,0 +1,238 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f73a28ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"KENLM_BUILD_PATH='/home/students/s434708/kenlm/build'"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9fc5cda3",
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"metadata": {},
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"source": [
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"### Preprocessing danych"
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]
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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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"id": "d42ddd87",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import csv\n",
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"import regex as re"
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]
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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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"id": "f84be210",
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"metadata": {},
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"outputs": [],
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"source": [
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"def clean_text(text):\n",
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" text = text.lower().replace('-\\\\n', '').replace('\\\\n', ' ')\n",
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" text = re.sub(r'\\p{P}', '', text)\n",
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"\n",
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" return text"
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]
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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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"id": "de0c12d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
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"train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
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"\n",
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"train_data = train_data[[6, 7]]\n",
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"train_data = pd.concat([train_data, train_labels], axis=1)\n",
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"\n",
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"train_data['text'] = train_data[6] + train_data[0] + train_data[7]\n",
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"train_data = train_data[['text']]\n",
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"\n",
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"with open('processed_train.txt', 'w') as file:\n",
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" for _, row in train_data.iterrows():\n",
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" text = clean_text(str(row['text']))\n",
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" file.write(text + '\\n')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "846b6b42",
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"metadata": {},
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"source": [
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"### Model kenLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3c74d4be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"=== 1/5 Counting and sorting n-grams ===\n",
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"Reading /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/processed_train.txt\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"********************************Warning: <s> appears in the input. All instances of <s>, </s>, and <unk> will be interpreted as whitespace.\n",
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"********************************************************************\n",
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"Unigram tokens 135911223 types 4381594\n",
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"=== 2/5 Calculating and sorting adjusted counts ===\n",
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"Chain sizes: 1:52579128 2:1295655936 3:2429355008 4:3886967808 5:5668495360\n",
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"Statistics:\n",
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"1 4381594 D1=0.841838 D2=1.01787 D3+=1.21057\n",
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"2 26800631 D1=0.836734 D2=1.01657 D3+=1.19437\n",
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"3 69811700 D1=0.878562 D2=1.11227 D3+=1.27889\n",
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"4 104063034 D1=0.931257 D2=1.23707 D3+=1.36664\n",
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"5 119487533 D1=0.938146 D2=1.3058 D3+=1.41614\n",
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"Memory estimate for binary LM:\n",
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"type MB\n",
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"probing 6752 assuming -p 1.5\n",
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"probing 7917 assuming -r models -p 1.5\n",
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"trie 3572 without quantization\n",
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"trie 2120 assuming -q 8 -b 8 quantization \n",
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"trie 3104 assuming -a 22 array pointer compression\n",
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"trie 1652 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
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"=== 3/5 Calculating and sorting initial probabilities ===\n",
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"Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"####################################################################################################\n",
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"=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
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"Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"####################################################################################################\n",
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"=== 5/5 Writing ARPA model ===\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"----------------------------------------------------------------------------------------------------Last input should have been poison. The program should end soon with an error. If it doesn't, there's a bug.\n",
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"terminate called after throwing an instance of 'util::FDException'\n",
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" what(): /home/students/s434708/kenlm/util/file.cc:228 in void util::WriteOrThrow(int, const void*, std::size_t) threw FDException because `ret < 1'.\n",
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"No space left on device in /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/model.arpa while writing 8189 bytes\n",
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"/bin/bash: line 1: 26725 Aborted /home/students/s434708/kenlm/build/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa\n"
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]
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}
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],
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"source": [
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"!$KENLM_BUILD_PATH/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "dc65780b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading model.arpa\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"****************************************************************************************************\n",
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"/home/students/s434708/kenlm/util/file.cc:86 in int util::CreateOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 0100 | 01000 | 02, 0400 | 0200 | (0400 >> 3) | ((0400 >> 3) >> 3)))'.\n",
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"No space left on device while creating model.binary Byte: 94\n",
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"ERROR\n"
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]
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}
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],
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"source": [
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"!$KENLM_BUILD_PATH/bin/build_binary model.arpa model.binary"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "2087eb80",
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"metadata": {},
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"outputs": [],
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"source": [
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"!rm processed_train.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "4ba1e592",
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"metadata": {},
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"outputs": [],
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"source": [
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"!rm model.arpa"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e41f7951",
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"metadata": {},
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"source": [
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"### Predykcje"
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]
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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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"id": "6865301b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import kenlm"
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]
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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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"id": "e32de662",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_str = 'really good'\n",
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"\n",
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"model = kenlm.Model('model.binary')\n",
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"print(model.score(test_str, bos = True, eos = True))"
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]
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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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"id": "a18b6ebd",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in model.full_scores(test_str):\n",
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" print(i)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,2 +1,9 @@
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# challenging-america-word-gap-prediction-kenlm
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Challenging America word-gap prediction - s434708
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===================================
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Guess a word in a gap.
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Evaluation metric
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-----------------
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LikelihoodHashed is the metric
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1
config.txt
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1
config.txt
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--metric PerplexityHashed --precision 2 --in-header in-header.tsv --out-header out-header.tsv
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10519
dev-0/expected.tsv
Normal file
10519
dev-0/expected.tsv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
dev-0/in.tsv.xz
Normal file
BIN
dev-0/in.tsv.xz
Normal file
Binary file not shown.
1
in-header.tsv
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1
in-header.tsv
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@ -0,0 +1 @@
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FileId Year LeftContext RightContext
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1
out-header.tsv
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1
out-header.tsv
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@ -0,0 +1 @@
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Word
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25
process_test.py
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25
process_test.py
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import pandas as pd
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import csv
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import regex as re
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def clean_text(text):
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text = text.lower().replace('-\\n', '').replace('\\n', ' ')
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text = re.sub(r'\p{P}', '', text)
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return text
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train_data = pd.read_csv('train/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
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train_labels = pd.read_csv('train/expected.tsv', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
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train_data = train_data[[6, 7]]
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train_data = pd.concat([train_data, train_labels], axis=1)
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train_data['text'] = train_data[6] + train_data[0] + train_data[7]
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train_data = train_data[['text']]
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with open('processed_train.txt', 'w') as file:
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for _, row in train_data.iterrows():
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text = clean_text(str(row['text']))
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file.write(text + '\n')
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238
run.ipynb
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238
run.ipynb
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@ -0,0 +1,238 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f73a28ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"KENLM_BUILD_PATH='/home/students/s434708/kenlm/build'"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9fc5cda3",
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"metadata": {},
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"source": [
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"### Preprocessing danych"
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]
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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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"id": "d42ddd87",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import csv\n",
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"import regex as re"
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]
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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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"id": "f84be210",
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"metadata": {},
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"outputs": [],
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"source": [
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"def clean_text(text):\n",
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" text = text.lower().replace('-\\\\n', '').replace('\\\\n', ' ')\n",
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" text = re.sub(r'\\p{P}', '', text)\n",
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"\n",
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" return text"
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]
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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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"id": "de0c12d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
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"train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
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"\n",
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"train_data = train_data[[6, 7]]\n",
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"train_data = pd.concat([train_data, train_labels], axis=1)\n",
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"\n",
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"train_data['text'] = train_data[6] + train_data[0] + train_data[7]\n",
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"train_data = train_data[['text']]\n",
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"\n",
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"with open('processed_train.txt', 'w') as file:\n",
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" for _, row in train_data.iterrows():\n",
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" text = clean_text(str(row['text']))\n",
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" file.write(text + '\\n')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "846b6b42",
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"metadata": {},
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"source": [
|
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"### Model kenLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3c74d4be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"=== 1/5 Counting and sorting n-grams ===\n",
|
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"Reading /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/processed_train.txt\n",
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"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"********************************Warning: <s> appears in the input. All instances of <s>, </s>, and <unk> will be interpreted as whitespace.\n",
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"********************************************************************\n",
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"Unigram tokens 135911223 types 4381594\n",
|
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"=== 2/5 Calculating and sorting adjusted counts ===\n",
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"Chain sizes: 1:52579128 2:1295655936 3:2429355008 4:3886967808 5:5668495360\n",
|
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"Statistics:\n",
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"1 4381594 D1=0.841838 D2=1.01787 D3+=1.21057\n",
|
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"2 26800631 D1=0.836734 D2=1.01657 D3+=1.19437\n",
|
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"3 69811700 D1=0.878562 D2=1.11227 D3+=1.27889\n",
|
||||
"4 104063034 D1=0.931257 D2=1.23707 D3+=1.36664\n",
|
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"5 119487533 D1=0.938146 D2=1.3058 D3+=1.41614\n",
|
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"Memory estimate for binary LM:\n",
|
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"type MB\n",
|
||||
"probing 6752 assuming -p 1.5\n",
|
||||
"probing 7917 assuming -r models -p 1.5\n",
|
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"trie 3572 without quantization\n",
|
||||
"trie 2120 assuming -q 8 -b 8 quantization \n",
|
||||
"trie 3104 assuming -a 22 array pointer compression\n",
|
||||
"trie 1652 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
|
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"=== 3/5 Calculating and sorting initial probabilities ===\n",
|
||||
"Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
|
||||
"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
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"####################################################################################################\n",
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"=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
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"Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
|
||||
"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
|
||||
"####################################################################################################\n",
|
||||
"=== 5/5 Writing ARPA model ===\n",
|
||||
"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
|
||||
"----------------------------------------------------------------------------------------------------Last input should have been poison. The program should end soon with an error. If it doesn't, there's a bug.\n",
|
||||
"terminate called after throwing an instance of 'util::FDException'\n",
|
||||
" what(): /home/students/s434708/kenlm/util/file.cc:228 in void util::WriteOrThrow(int, const void*, std::size_t) threw FDException because `ret < 1'.\n",
|
||||
"No space left on device in /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/model.arpa while writing 8189 bytes\n",
|
||||
"/bin/bash: line 1: 26725 Aborted /home/students/s434708/kenlm/build/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!$KENLM_BUILD_PATH/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "dc65780b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Reading model.arpa\n",
|
||||
"----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
|
||||
"****************************************************************************************************\n",
|
||||
"/home/students/s434708/kenlm/util/file.cc:86 in int util::CreateOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 0100 | 01000 | 02, 0400 | 0200 | (0400 >> 3) | ((0400 >> 3) >> 3)))'.\n",
|
||||
"No space left on device while creating model.binary Byte: 94\n",
|
||||
"ERROR\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!$KENLM_BUILD_PATH/bin/build_binary model.arpa model.binary"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "2087eb80",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!rm processed_train.txt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "4ba1e592",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!rm model.arpa"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e41f7951",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Predykcje"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6865301b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import kenlm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e32de662",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_str = 'really good'\n",
|
||||
"\n",
|
||||
"model = kenlm.Model('model.binary')\n",
|
||||
"print(model.score(test_str, bos = True, eos = True))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a18b6ebd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in model.full_scores(test_str):\n",
|
||||
" print(i)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
test-A/in.tsv.xz
Normal file
BIN
test-A/in.tsv.xz
Normal file
Binary file not shown.
432022
train/expected.tsv
Normal file
432022
train/expected.tsv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
train/in.tsv.xz
Normal file
BIN
train/in.tsv.xz
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user