tetragrams-added
This commit is contained in:
parent
1f742b4802
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ab4d36fca9
372
MOJ5.ipynb
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372
MOJ5.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4",
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"machine_shape": "hm"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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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": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "3dV_4SJ2xY_C",
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"outputId": "28f0b228-e536-410e-8d45-a2063a04455b"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mounted at /content/gdrive\n"
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]
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}
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],
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"source": [
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"from google.colab import drive\n",
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"drive.mount(\"/content/gdrive\")"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# %env DATA_DIR=/content/gdrive/MyDrive/data_gralinski\n",
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"DATA_DIR=\"/content/gdrive/MyDrive/data_gralinski/\""
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],
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"metadata": {
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"id": "VwdW1Qm3x9-N"
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},
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import lzma\n",
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"import pickle\n",
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"from collections import Counter"
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],
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"metadata": {
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"id": "irsty5KcyYkR"
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},
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def clean_line(line: str):\n",
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" separated = line.split('\\t')\n",
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" prefix = separated[6].replace(r'\\n', ' ')\n",
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" suffix = separated[7].replace(r'\\n', ' ')\n",
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" return prefix + ' ' + suffix"
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],
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"metadata": {
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"id": "LXXtiKW3yY5J"
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},
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def words(filename):\n",
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" with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
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" index = 1\n",
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" print('Words')\n",
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" for line in fid:\n",
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" print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
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" text = clean_line(line)\n",
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" for word in text.split():\n",
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" yield word\n",
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" index += 1\n",
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" print()\n"
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],
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"metadata": {
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"id": "y9r0wmD3ycIi"
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},
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def bigrams(filename, V: dict):\n",
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" with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
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" index = 1\n",
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" print('Bigrams')\n",
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" for line in fid:\n",
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" print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
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" text = clean_line(line)\n",
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" first_word = ''\n",
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" for second_word in text.split():\n",
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" if V.get(second_word) is None:\n",
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" second_word = 'UNK'\n",
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" if second_word:\n",
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" yield first_word, second_word\n",
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" first_word = second_word\n",
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" index += 1\n",
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" print()"
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],
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"metadata": {
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"id": "HE3YfiHkycKt"
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},
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"execution_count": 6,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def trigrams(filename, V: dict):\n",
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" with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
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" index = 1\n",
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" print('Trigrams')\n",
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" for line in fid:\n",
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" print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
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" text = clean_line(line)\n",
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" first_word = ''\n",
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" second_word = ''\n",
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" for third_word in text.split():\n",
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" if V.get(third_word) is None:\n",
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" third_word = 'UNK'\n",
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" if first_word:\n",
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" yield first_word, second_word, third_word\n",
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" first_word = second_word\n",
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" second_word = third_word\n",
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" index += 1\n",
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" print()"
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],
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"metadata": {
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"id": "lvHvJV6XycNZ"
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},
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def tetragrams(filename, V: dict):\n",
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" with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
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" index = 1\n",
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" print('Tetragrams')\n",
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" for line in fid:\n",
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" print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
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" text = clean_line(line)\n",
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" first_word = ''\n",
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" second_word = ''\n",
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" third_word = ''\n",
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" for fourth_word in text.split():\n",
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" if V.get(fourth_word) is None:\n",
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" fourth_word = 'UNK'\n",
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" if first_word:\n",
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" yield first_word, second_word, third_word, fourth_word\n",
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" first_word = second_word\n",
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" second_word = third_word\n",
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" third_word = fourth_word\n",
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" index += 1\n",
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" print()"
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],
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"metadata": {
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"id": "sOKeZN9cycP-"
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},
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def P(first_word, second_word=None, third_word=None, fourth_word=None):\n",
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" try:\n",
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" if second_word is None:\n",
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" return V_common_dict[first_word] / total\n",
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" if third_word is None:\n",
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" return V2_dict[(first_word, second_word)] / V_common_dict[first_word]\n",
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" if fourth_word is None:\n",
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" return V3_dict[(first_word, second_word, third_word)] / V2_dict[(first_word, second_word)]\n",
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" else:\n",
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" return V4_dict[(first_word, second_word, third_word, fourth_word)] / V3_dict[\n",
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" (first_word, second_word, third_word)]\n",
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" except KeyError:\n",
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" return 0"
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],
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"metadata": {
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"id": "MN_RftZNycSB"
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},
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def smoothed(tetragram):\n",
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" first, second, third, fourth = tetragram\n",
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" return 0.5 * P(first, second, third, fourth) + 0.25 * P(second, third, fourth) + 0.15 * P(third, fourth) + 0.1 * P(\n",
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" fourth)"
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],
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"metadata": {
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"id": "n9wIsbLEycUd"
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},
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"execution_count": 10,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def candidates(left_context, right_context):\n",
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" cand = {}\n",
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" first, second, third = left_context\n",
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" fifth, sixth, seventh = right_context\n",
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" for word in V_common_dict:\n",
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" p1 = smoothed((first, second, third, word))\n",
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" p2 = smoothed((second, third, word, fifth))\n",
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" p3 = smoothed((third, word, fifth, sixth))\n",
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" p4 = smoothed((word, fifth, sixth, seventh))\n",
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" cand[word] = p1 * p2 * p3 * p4\n",
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" cand = sorted(list(cand.items()), key=lambda x: x[1], reverse=True)[:5]\n",
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" norm = [(x[0], float(x[1]) / sum([y[1] for y in cand])) for x in cand]\n",
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" for index, elem in enumerate(norm):\n",
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" unk = None\n",
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" if 'UNK' in elem:\n",
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" unk = norm.pop(index)\n",
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" norm.append(('', unk[1]))\n",
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" break\n",
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" if unk is None:\n",
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" norm[-1] = ('', norm[-1][1])\n",
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" return ' '.join([f'{x[0]}:{x[1]}' for x in norm])"
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],
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"metadata": {
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"id": "l490B5KFycXj"
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},
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"execution_count": 11,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def outputs(folder_name):\n",
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" print(f'Creating outputs in {folder_name}')\n",
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" with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
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" with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8') as f:\n",
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" for line in fid:\n",
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" separated = line.split('\\t')\n",
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" prefix = separated[6].replace(r'\\n', ' ').split()\n",
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" suffix = separated[7].replace(r'\\n', ' ').split()\n",
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" left_context = [x if V_common_dict.get(x) else 'UNK' for x in prefix[-3:]]\n",
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" right_context = [x if V_common_dict.get(x) else 'UNK' for x in suffix[:3]]\n",
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" w = candidates(left_context, right_context)\n",
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" f.write(w + '\\n')"
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],
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"metadata": {
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"id": "mMC84-OzycZ5"
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},
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"execution_count": 12,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"WORD_LIMIT = 3000\n",
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"V = Counter(words(DATA_DIR+'train/in.tsv.xz'))\n",
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"V_common_dict = dict(V.most_common(WORD_LIMIT))\n",
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"# UNK = 0\n",
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"# for key, value in V.items():\n",
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"# if V_common_dict.get(key) is None:\n",
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"# UNK += value\n",
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"# V_common_dict['UNK'] = UNK\n",
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"# with open('V.pickle', 'wb') as handle:\n",
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"# pickle.dump(V_common_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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"\n",
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"\n",
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"with open(DATA_DIR+'5/V.pickle', 'rb') as handle:\n",
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" V_common_dict = pickle.load(handle)\n",
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"\n",
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"total = sum(V_common_dict.values())\n",
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"\n",
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"# V2 = Counter(bigrams('train/in.tsv.xz', V_common_dict))\n",
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"# V2_dict = dict(V2)\n",
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"# with open('V2.pickle', 'wb') as handle:\n",
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"# pickle.dump(V2_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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"\n",
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"with open(DATA_DIR+'5/V2.pickle', 'rb') as handle:\n",
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" V2_dict = pickle.load(handle)\n",
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"\n",
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"# V3 = Counter(trigrams('train/in.tsv.xz', V_common_dict))\n",
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"# V3_dict = dict(V3)\n",
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"# with open('V3.pickle', 'wb') as handle:\n",
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"# pickle.dump(V3_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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"\n",
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"with open(DATA_DIR+'5/V3.pickle', 'rb') as handle:\n",
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" V3_dict = pickle.load(handle)\n",
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"\n",
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"V4 = Counter(tetragrams(DATA_DIR+'train/in.tsv.xz', V_common_dict))\n",
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"V4_dict = dict(V4)\n",
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"with open('V4.pickle', 'wb') as handle:\n",
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" pickle.dump(V4_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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"\n",
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"# with open('V4.pickle', 'rb') as handle:\n",
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"# V4_dict = pickle.load(handle)\n",
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"\n",
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"\n"
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],
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"metadata": {
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"id": "Fsvv3QJl7kWN",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "3c8387a4-5ebe-41ae-aafa-2bf3999f0025"
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},
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"execution_count": 15,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Words\n",
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"Progress: 100.000000%\n",
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"Tetragrams\n",
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"Progress: 100.000000%\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"outputs(DATA_DIR+'dev-0')\n",
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"outputs(DATA_DIR+'test-A')\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "UK73WsKnB8ZP",
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"outputId": "2c3d6171-bfb2-4fcd-cfc0-49cabdf9f0a9"
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},
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"execution_count": 17,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Creating outputs in /content/gdrive/MyDrive/data_gralinski/dev-0\n",
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"Creating outputs in /content/gdrive/MyDrive/data_gralinski/test-A\n"
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]
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}
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]
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}
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]
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}
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10519
dev-0/out.tsv
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10519
dev-0/out.tsv
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File diff suppressed because it is too large
Load Diff
10
gonito.yaml
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gonito.yaml
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description: trigram model
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tags:
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- neural-network
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- trigram
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params:
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epochs: 1
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learning-rate: 0.0001
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vocab_size: 40000
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embed_size: 300
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hidden_size: 256
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6321
test-A/out.tsv
Normal file
6321
test-A/out.tsv
Normal file
File diff suppressed because one or more lines are too long
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Block a user