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| } | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/emillykkejensen/8bf1b323495efc7252dee966e6bc1b5c/notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "oh2xfITDhN2u" | |
| }, | |
| "source": [ | |
| "Denne notebook er rent copy-paste fra https://gist.github.com/avidale/44cd35bfcdaf8bedf51d97c468cc8001 - lavet af David Dale. Før du kaster dig over den, så læs lige den artikkel han har skrevet på towardsdatascience: https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90\n", | |
| "\n", | |
| "Pointen med det her skriv er, at reducere mT5 modellen, så den kun indeholder danske embedings og dermed gerne skulle reducere modellens størrelse ret markant!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BoiF06nfGvtW", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "a6be3f19-0e00-4489-ad6e-8d657df0656c" | |
| }, | |
| "source": [ | |
| "!pip install transformers sentencepiece" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting transformers\n", | |
| " Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n", | |
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| " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n", | |
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| "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece\n", | |
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| " Found existing installation: PyYAML 3.13\n", | |
| " Uninstalling PyYAML-3.13:\n", | |
| " Successfully uninstalled PyYAML-3.13\n", | |
| "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.15.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zcVexj3Ye6X3" | |
| }, | |
| "source": [ | |
| "# Hvordan ser det ud" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "X99M7UWoHC9k" | |
| }, | |
| "source": [ | |
| "from transformers import MT5ForConditionalGeneration, MT5Tokenizer\n", | |
| "import torch\n", | |
| "\n", | |
| "model_name = 'google/mt5-base'\n", | |
| "output_name = 'emillykkejensen/daT5-base'" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 162, | |
| "referenced_widgets": [ | |
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| "outputId": "46eb89bf-c393-455f-9fa1-0cc022c8124f" | |
| }, | |
| "source": [ | |
| "tokenizer = MT5Tokenizer.from_pretrained(model_name)\n", | |
| "len(tokenizer.get_vocab())\n", | |
| "\n", | |
| "# Her skal den ikke hentes som fast, da det skaber problemer senere.." | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "7cb4b97fba8d401f99b0e99cc984a7d7", | |
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| { | |
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| { | |
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| "data": { | |
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| "250100" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YMItls1shI3-" | |
| }, | |
| "source": [ | |
| "Den originale m5t token indeholder 250K tokens + 100 t5 tokens " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import AutoTokenizer\n", | |
| "tokenizerDA = AutoTokenizer.from_pretrained('Maltehb/danish-bert-botxo')\n", | |
| "tokenizerDA" | |
| ], | |
| "metadata": { | |
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| { | |
| "output_type": "display_data", | |
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| { | |
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| }, | |
| { | |
| "output_type": "display_data", | |
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| { | |
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| "text/plain": [ | |
| "PreTrainedTokenizerFast(name_or_path='Maltehb/danish-bert-botxo', vocab_size=31748, model_max_len=512, is_fast=True, padding_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Hvis vi henter Malte Højmark-Bertelsens dansk trænet BERT models tokens, så indeholder den \"kun\" 31748 tokens." | |
| ], | |
| "metadata": { | |
| "id": "3l_dmDVPUwdN" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "HkXHkM6OHJcH", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 240, | |
| "referenced_widgets": [ | |
| "ff02249764fd486aa76b85fd0b897fa2", | |
| "d2593ed063b04c8393540238099106fb", | |
| "62b5aa68bd5843fdb86766f69579906a", | |
| "8879dc4f5ac04276add412cb45e33778", | |
| "d420cb55bd5a431cbca36ba13f58646c", | |
| "2b660317227d4fef83fa0f3454531f46", | |
| "9d0aa21709794fd9abb431cecbd47409", | |
| "e58c3a5a125a47c6b1008236907e7657", | |
| "837e25dcfa094941aef01f735cf03683", | |
| "21ccd08a71064febaaf7fdd369cf73a8", | |
| "b4ba4629fdaa45f08228f89becdc8257" | |
| ] | |
| }, | |
| "outputId": "22a79855-57f0-480a-8534-bb9381f98a83" | |
| }, | |
| "source": [ | |
| "model = MT5ForConditionalGeneration.from_pretrained(model_name)\n", | |
| "\n", | |
| "def msize(m):\n", | |
| " return sum(p.numel() for p in m.parameters())\n", | |
| "\n", | |
| "original_size = msize(model)\n", | |
| "print(msize(model))\n", | |
| "print(msize(model.shared))\n", | |
| "print('\\nEncoder:')\n", | |
| "print(msize(model.encoder))\n", | |
| "print(msize(model.encoder.block))\n", | |
| "print('\\nDecoder:')\n", | |
| "print(msize(model.decoder))\n", | |
| "print(msize(model.decoder.block))\n", | |
| "print(msize(model.lm_head))" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "ff02249764fd486aa76b85fd0b897fa2", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/2.17G [00:00<?, ?B/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "582401280\n", | |
| "192086016\n", | |
| "\n", | |
| "Encoder:\n", | |
| "277040256\n", | |
| "84953472\n", | |
| "\n", | |
| "Decoder:\n", | |
| "305361024\n", | |
| "113274240\n", | |
| "192086016\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Den originale model indeholder 582k embeddings og fylder hele 2,1 Gb" | |
| ], | |
| "metadata": { | |
| "id": "crFF58H4Td9_" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hmvmyYsyHh2s", | |
| "outputId": "dcffce97-140b-4db7-e9b5-6bf69245751e" | |
| }, | |
| "source": [ | |
| "print(msize(model.shared) / msize(model))\n", | |
| "print(msize(model.lm_head) / msize(model))" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.32981729710484153\n", | |
| "0.32981729710484153\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "18ckhebWLLra" | |
| }, | |
| "source": [ | |
| "Ud af dem, udgør Input og output embeddings 66%" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "amFXHV9OL9SU" | |
| }, | |
| "source": [ | |
| "# Kig på tokens" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NfeGCTv5Vvmu" | |
| }, | |
| "source": [ | |
| "Hent tekst corpus fra https://wortschatz.uni-leipzig.de/en/download/Danish på dansk og engelsk. I modsætning til Davids originale script, så henter jeg nyheder og ikke web-public - primært for at få et 'renere' sprog." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "WxsNhpKfME5W", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "95a14355-bb96-426c-baeb-7e97965d9afe" | |
| }, | |
| "source": [ | |
| "!wget http://pcai056.informatik.uni-leipzig.de/downloads/corpora/dan_news_2020_1M.tar.gz\n", | |
| "!tar -xsvf dan_news_2020_1M.tar.gz" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-01-06 10:03:19-- http://pcai056.informatik.uni-leipzig.de/downloads/corpora/dan_news_2020_1M.tar.gz\n", | |
| "Resolving pcai056.informatik.uni-leipzig.de (pcai056.informatik.uni-leipzig.de)... 139.18.2.216\n", | |
| "Connecting to pcai056.informatik.uni-leipzig.de (pcai056.informatik.uni-leipzig.de)|139.18.2.216|:80... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 210729442 (201M) [application/x-gzip]\n", | |
| "Saving to: ‘dan_news_2020_1M.tar.gz’\n", | |
| "\n", | |
| "dan_news_2020_1M.ta 100%[===================>] 200.97M 73.6MB/s in 2.7s \n", | |
| "\n", | |
| "2022-01-06 10:03:21 (73.6 MB/s) - ‘dan_news_2020_1M.tar.gz’ saved [210729442/210729442]\n", | |
| "\n", | |
| "dan_news_2020_1M/\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-co_s.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-inv_w.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-words.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-inv_so.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-import.sql\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-sentences.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-co_n.txt\n", | |
| "dan_news_2020_1M/dan_news_2020_1M-sources.txt\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "XNHwPMCHiRhr", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "7e0c3fcc-2d79-4143-94b8-e35de23f0c81" | |
| }, | |
| "source": [ | |
| "!wget http://pcai056.informatik.uni-leipzig.de/downloads/corpora/eng_news_2020_1M.tar.gz\n", | |
| "!tar -xsvf eng_news_2020_1M.tar.gz" | |
| ], | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-01-06 10:03:27-- http://pcai056.informatik.uni-leipzig.de/downloads/corpora/eng_news_2020_1M.tar.gz\n", | |
| "Resolving pcai056.informatik.uni-leipzig.de (pcai056.informatik.uni-leipzig.de)... 139.18.2.216\n", | |
| "Connecting to pcai056.informatik.uni-leipzig.de (pcai056.informatik.uni-leipzig.de)|139.18.2.216|:80... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 276283393 (263M) [application/x-gzip]\n", | |
| "Saving to: ‘eng_news_2020_1M.tar.gz’\n", | |
| "\n", | |
| "eng_news_2020_1M.ta 100%[===================>] 263.48M 100MB/s in 2.6s \n", | |
| "\n", | |
| "2022-01-06 10:03:30 (100 MB/s) - ‘eng_news_2020_1M.tar.gz’ saved [276283393/276283393]\n", | |
| "\n", | |
| "eng_news_2020_1M/\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-import.sql\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-words.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-inv_w.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-sentences.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-sources.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-inv_so.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-co_s.txt\n", | |
| "eng_news_2020_1M/eng_news_2020_1M-co_n.txt\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gqjTHFJIiZTk" | |
| }, | |
| "source": [ | |
| "Load corpus ind i pandas og tag et lille kig på data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 441 | |
| }, | |
| "id": "IoJlXMw_M7pT", | |
| "outputId": "670eb1ee-d578-4c4a-ab98-065486b78c39" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import csv\n", | |
| "\n", | |
| "# Brug Googles Colabs data_table til at vise tabellen interaktivt\n", | |
| "from google.colab import data_table\n", | |
| "data_table.enable_dataframe_formatter()\n", | |
| "\n", | |
| "fname = 'dan_news_2020_1M/dan_news_2020_1M-sentences.txt'\n", | |
| "df_da = pd.read_csv(fname, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_da.columns = ['idx', 'text']\n", | |
| "df_da" | |
| ], | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Warning: total number of rows (1000000) exceeds max_rows (20000). Falling back to pandas display.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-f5464d68-01e4-49e6-bbad-dec6db913ad6\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>idx</th>\n", | |
| " <th>text</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>’0’ bliver defineret som »Jeg gør aldrig noget...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>”10 gode grunde til at elske hvaler”</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>\"10 tak 2660\" går ud på at takke alt fra læger...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>»10- til 14-årige ved måske, at de har gjort n...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>»112 skal man kun ringe, når man er i akut far...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999995</th>\n", | |
| " <td>999996</td>\n", | |
| " <td>Zwicky har valgt ikke at inddrage de svar, jeg...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999996</th>\n", | |
| " <td>999997</td>\n", | |
| " <td>Zydeco er en tempofyldt musikform, som har tæt...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999997</th>\n", | |
| " <td>999998</td>\n", | |
| " <td>Zydeco er forholdsvis sjælden på disse breddeg...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999998</th>\n", | |
| " <td>999999</td>\n", | |
| " <td>ZZ Top Jam livestreamer torsdag den 23. april ...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999999</th>\n", | |
| " <td>1000000</td>\n", | |
| " <td>ZZ Top Jam spiller efter borgmesterens tale.</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1000000 rows × 2 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5464d68-01e4-49e6-bbad-dec6db913ad6')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-f5464d68-01e4-49e6-bbad-dec6db913ad6 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-f5464d68-01e4-49e6-bbad-dec6db913ad6');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ], | |
| "text/plain": [ | |
| " idx text\n", | |
| "0 1 ’0’ bliver defineret som »Jeg gør aldrig noget...\n", | |
| "1 2 ”10 gode grunde til at elske hvaler”\n", | |
| "2 3 \"10 tak 2660\" går ud på at takke alt fra læger...\n", | |
| "3 4 »10- til 14-årige ved måske, at de har gjort n...\n", | |
| "4 5 »112 skal man kun ringe, når man er i akut far...\n", | |
| "... ... ...\n", | |
| "999995 999996 Zwicky har valgt ikke at inddrage de svar, jeg...\n", | |
| "999996 999997 Zydeco er en tempofyldt musikform, som har tæt...\n", | |
| "999997 999998 Zydeco er forholdsvis sjælden på disse breddeg...\n", | |
| "999998 999999 ZZ Top Jam livestreamer torsdag den 23. april ...\n", | |
| "999999 1000000 ZZ Top Jam spiller efter borgmesterens tale.\n", | |
| "\n", | |
| "[1000000 rows x 2 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 441 | |
| }, | |
| "id": "V-Uc7nbziyXp", | |
| "outputId": "0505102d-d838-44d5-fe9d-c80b37248b0e" | |
| }, | |
| "source": [ | |
| "fname = 'eng_news_2020_1M/eng_news_2020_1M-sentences.txt'\n", | |
| "df_en = pd.read_csv(fname, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_en.columns = ['idx', 'text']\n", | |
| "df_en" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Warning: total number of rows (1000000) exceeds max_rows (20000). Falling back to pandas display.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-29b851bc-0d32-4fe2-8b8b-29eec0b8811c\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
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| " <td>1</td>\n", | |
| " <td>!, 05/27/1994) — NHL Network, 2 p.m. & 10 p.m.</td>\n", | |
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| " <th>1</th>\n", | |
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| " <tr>\n", | |
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| " <td>3</td>\n", | |
| " <td>€100,000 in cash was seized during the joint o...</td>\n", | |
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| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>$100,000 to Hospice Santa Cruz County and $75,...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>£1,000 bonus 'may not be enough to protect jobs'</td>\n", | |
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| " <tr>\n", | |
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| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999995</th>\n", | |
| " <td>999996</td>\n", | |
| " <td>အောက်တွင် မြန်မာလို ဖတ်ရှုနိုင်ပါသည် Myanmar C...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999996</th>\n", | |
| " <td>999997</td>\n", | |
| " <td>一The former downtown Magpie Cafe space was tak...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999997</th>\n", | |
| " <td>999998</td>\n", | |
| " <td>免疫實踐諮詢委員會（Advisory Committee on Immunization P...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999998</th>\n", | |
| " <td>999999</td>\n", | |
| " <td>日本リサーチセンター Nippon Research Center, Ltd.</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>999999</th>\n", | |
| " <td>1000000</td>\n", | |
| " <td>食戟のソーマ4th Plate was awesome!</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1000000 rows × 2 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29b851bc-0d32-4fe2-8b8b-29eec0b8811c')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
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| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
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| " .colab-df-convert {\n", | |
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| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-29b851bc-0d32-4fe2-8b8b-29eec0b8811c button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
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| " const element = document.querySelector('#df-29b851bc-0d32-4fe2-8b8b-29eec0b8811c');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
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| "text/plain": [ | |
| " idx text\n", | |
| "0 1 !, 05/27/1994) — NHL Network, 2 p.m. & 10 p.m.\n", | |
| "1 2 $0 spent on advertising sans the rare times we...\n", | |
| "2 3 €100,000 in cash was seized during the joint o...\n", | |
| "3 4 $100,000 to Hospice Santa Cruz County and $75,...\n", | |
| "4 5 £1,000 bonus 'may not be enough to protect jobs'\n", | |
| "... ... ...\n", | |
| "999995 999996 အောက်တွင် မြန်မာလို ဖတ်ရှုနိုင်ပါသည် Myanmar C...\n", | |
| "999996 999997 一The former downtown Magpie Cafe space was tak...\n", | |
| "999997 999998 免疫實踐諮詢委員會（Advisory Committee on Immunization P...\n", | |
| "999998 999999 日本リサーチセンター Nippon Research Center, Ltd.\n", | |
| "999999 1000000 食戟のソーマ4th Plate was awesome!\n", | |
| "\n", | |
| "[1000000 rows x 2 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zhkWqfdNjNww" | |
| }, | |
| "source": [ | |
| "Encode hver sætning og tæl antallet af gange en token bliver brugt for hvert corpus (dansk og engelsk)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81, | |
| "referenced_widgets": [ | |
| "8be7375611d04b4dab1925150addffc4", | |
| "6a20ac71356840cd99028f437b5cb387", | |
| "5da72ccbfa8a4a8a8f1fa9b6abcb7af5", | |
| "40c503e339dc4960981e5d1591bb2619", | |
| "53279568c10f4af6808219621022dbe8", | |
| "b65b1c4b41534a908176baef5133f37a", | |
| "8ffa38100a6c44b6ab2b372586cf2029", | |
| "875a85d050754cc98e382fd7e07c48ef", | |
| "e76a0ef044ab49eaae844a14a482f909", | |
| "8e93af770b1b4895aa6ff7eb97a50875", | |
| "86128334b1fe4fd58507e2c9a8eeb525", | |
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| "381356f5f1f54eaea7383c82aac12e87", | |
| "af518f9b4405463781dd3be1c4a33c40", | |
| "0ba2bba408fe4efa86380a1fd215429e", | |
| "b65ce4fef67f41b6bed67d15b92b38bc", | |
| "b8681aba32c44c70aa07c967a89311d8", | |
| "f779fe22ed5946e896427923fe8b2f7a", | |
| "948a35328c3247b4be0b81504c0a5a2b", | |
| "5d76be2426c14c0da8fcf7ce072281db", | |
| "7da188e06e5846cbb98a29c9c6ebf240" | |
| ] | |
| }, | |
| "id": "lmzSON9iM_yb", | |
| "outputId": "f5c675f6-ad6e-4953-834b-d4593c5a6d9d" | |
| }, | |
| "source": [ | |
| "from collections import Counter\n", | |
| "from tqdm.auto import tqdm, trange\n", | |
| "\n", | |
| "cnt_da = Counter()\n", | |
| "for text in tqdm(df_da.text):\n", | |
| " cnt_da.update(tokenizer.encode(text))\n", | |
| "\n", | |
| "cnt_en = Counter()\n", | |
| "for text in tqdm(df_en.text):\n", | |
| " cnt_en.update(tokenizer.encode(text))" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "8be7375611d04b4dab1925150addffc4", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| " 0%| | 0/1000000 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "a084a3da9ccd458b9939dc8c90ecee01", | |
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| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| " 0%| | 0/1000000 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "sTzND5F1OkEY" | |
| }, | |
| "source": [ | |
| "Find ud af, hvor mange gange tokens bruges i hvert sprog:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "M07fj3z0NWiy", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "3e871ffa-be2f-41c9-9baa-53acebb25a0f" | |
| }, | |
| "source": [ | |
| "print('DA: ', len(cnt_da), len(cnt_da)/tokenizer.vocab_size)\n", | |
| "print('EN: ', len(cnt_en), len(cnt_en)/tokenizer.vocab_size)\n", | |
| "common = len(set(cnt_da.keys()).intersection(set(cnt_en.keys())))\n", | |
| "print('Fælles: ', common, common / len(cnt_da))" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "DA: 51545 0.20609756097560974\n", | |
| "EN: 71744 0.2868612554978009\n", | |
| "Fælles: 41396 0.8031040838102629\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2ULUmyllmNA0" | |
| }, | |
| "source": [ | |
| "På dansk bruges 51k (20%) ud af de 250k tokens, mens det på engelsk er 71k (28%) - hvilket giver meget god mening, da engelsk indeholder flere ord end dansk og da den originale mt5 model er trænet på væsenligt mere engelsk end dansk data.\n", | |
| "\n", | |
| "41k tokens bruges både på dansk og engelsk - hvilket betyder at 80% af alle danske tokens også bruges på engelsk." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Nedenfor tjekker vi hvor hvor stor en andel top 10k, 20k og 30k mest brugte token, udgør af den samlede brug" | |
| ], | |
| "metadata": { | |
| "id": "fO9tw6niBtwq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kNudkAe5NbKT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "291b2811-a8b6-439c-9d28-17a804eacf8e" | |
| }, | |
| "source": [ | |
| "print('Dansk:')\n", | |
| "for top in 10_000, 20_000, 30_000:\n", | |
| " print('Top:', top, sum(v for k, v in cnt_da.most_common(top)) / sum(cnt_da.values()))\n", | |
| "print('\\nEngelsk:')\n", | |
| "for top in 10_000, 20_000, 30_000:\n", | |
| " print('Top:', top, sum(v for k, v in cnt_en.most_common(top)) / sum(cnt_en.values()))" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dansk:\n", | |
| "Top: 10000 0.982576739076921\n", | |
| "Top: 20000 0.9956902186266333\n", | |
| "Top: 30000 0.9986029349118826\n", | |
| "\n", | |
| "Engelsk:\n", | |
| "Top: 10000 0.9515249161343127\n", | |
| "Top: 20000 0.9823923151934567\n", | |
| "Top: 30000 0.9925520177286887\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Vi kan se, at 98% af alle tokens, der bruges på dansk, kan findes i top 10.000. På engelsk er det 95%. Hvis vi ser på top 20k så er næsten alle tokens på dansk brugt (99,5%)." | |
| ], | |
| "metadata": { | |
| "id": "xS0_kPy8B9wZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I forsøget på at medtage med de Danske BERT tokens, fjerner jeg de wordpiece specifikke '##' og encoder herefter hver BERT tokens til mt5 sentencepiece tokens.\n", | |
| "Er ikke 100% på at det her er den smarteste måde at gøre det på, så råb op hvis du har en bedre ide!" | |
| ], | |
| "metadata": { | |
| "id": "agMqjClv1Mth" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tokenizerDA_vocab = tokenizerDA.vocab\n", | |
| "\n", | |
| "# Fjern wordpiece ##\n", | |
| "import re\n", | |
| "new_tokenizerDA_vocab = {re.sub('^##', '', k): v for k, v in tokenizerDA_vocab.items()}\n", | |
| "\n", | |
| "# Encode de danske BERT tokens\n", | |
| "cnt_BERT_da = Counter()\n", | |
| "for text in tqdm(new_tokenizerDA_vocab.keys()):\n", | |
| " cnt_BERT_da.update(tokenizer.encode(text))\n", | |
| "\n", | |
| "cnt_da_top20k = cnt_da.most_common(20_000)\n", | |
| "cnt_da_top20k = [item[0] for item in cnt_da_top20k]\n", | |
| "\n", | |
| "BERTda_tokenizer_token_ids = set(cnt_BERT_da)\n", | |
| "tokenizer_da_token_ids = set(cnt_da_top20k)\n", | |
| "\n", | |
| "shared_tokens = tokenizer_da_token_ids.intersection(BERTda_tokenizer_token_ids)\n", | |
| "\n", | |
| "print('Antal tokens fra dansk BERT:', len(cnt_BERT_da))\n", | |
| "print('Antal tokens fra dansk BERT brugt i encoding af danske nyheder (top 20k):', len(shared_tokens))\n" | |
| ], | |
| "metadata": { | |
| "id": "A5jWg8wcdM8o", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 83, | |
| "referenced_widgets": [ | |
| "9ccd8c47002b43249f0e07459a5886f9", | |
| "d00bf7d8a3d0488eb344244249df0fe2", | |
| "fc5e059d553b4a72a53223ef235f1973", | |
| "6dd473af4dad45ea86641442e27174da", | |
| "ea4a96de78da4b939a2864125d5d90fd", | |
| "5258b3f8b92f4f9980f37c36d5d81950", | |
| "5667c1b0fd464a48a936177c661ae657", | |
| "4012da0c84f74026bae1e204e1480260", | |
| "34c2f6508dcc4b9789807425f88d9627", | |
| "99ec014a0d894b1988f86cbbf8e5887d", | |
| "2f313463742f4784a6e2783bb7acec55" | |
| ] | |
| }, | |
| "outputId": "a418e18d-2dc6-4759-d583-9a59e3a3f2da" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "9ccd8c47002b43249f0e07459a5886f9", | |
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| }, | |
| "text/plain": [ | |
| " 0%| | 0/26677 [00:00<?, ?it/s]" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Antal tokens fra dansk BERT: 11955\n", | |
| "Antal tokens fra dansk BERT brugt i encoding af danske nyheder (top 20k): 8969\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Med denne lille sammenligning kan vi se, at de 31748 token id's i den originale danske BERT tokenizer, bliver til 11955 når vi bruger mt5 tokens. Af dem bruges 8969 tokens id's også i top 20k encodings af danske nyheder." | |
| ], | |
| "metadata": { | |
| "id": "Zz2Ns2Rm-0cC" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0N_D37J3lbqr" | |
| }, | |
| "source": [ | |
| "Inden vi lukker og slukker for denne lille øvelse, kopieres det gamle vocab, da det skal bruges senere..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9RzGibfZQbgP" | |
| }, | |
| "source": [ | |
| "old_voc = tokenizer.get_vocab()\n", | |
| "old_inv_voc = {v: k for k, v in old_voc.items()}" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Byg de 'nye' tokens" | |
| ], | |
| "metadata": { | |
| "id": "2DHw3wUFC4iU" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "AwwPWiO3Po1x" | |
| }, | |
| "source": [ | |
| "Jeg sammensætter de 'nye' tokens (dvs. fjerner alle andre) på følgende måde:\n", | |
| "\n", | |
| "* Top 1K af de originale tokens (fordi han gør)\n", | |
| "* Alle tokens brugt til at encode BERT tokens.\n", | |
| "* Top 20K af de danske tokens (eller så det summer op til 30K)\n", | |
| "* 100 (m)T5 tokens\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "J-aSMIB1Pxvh", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "defeab33-2421-4811-f31f-64cce26f06f2" | |
| }, | |
| "source": [ | |
| "new_tokens = set(range(1000))\n", | |
| "print(len(new_tokens))\n", | |
| "\n", | |
| "for i, (k, v) in enumerate(cnt_BERT_da.items()):\n", | |
| " if k not in new_tokens:\n", | |
| " new_tokens.add(k)\n", | |
| "print(len(new_tokens))\n", | |
| "\n", | |
| "for i, (k, v) in enumerate(cnt_da.most_common(20_000)):\n", | |
| " if len(new_tokens) == 29_900:\n", | |
| " print(i, 'Danish tokens are included')\n", | |
| " break\n", | |
| " if k not in new_tokens:\n", | |
| " new_tokens.add(k)\n", | |
| "print(len(new_tokens))\n", | |
| "\n", | |
| "for t in range(tokenizer.vocab_size - 100, tokenizer.vocab_size):\n", | |
| " new_tokens.add(t)\n", | |
| "\n", | |
| "print(len(new_tokens))\n", | |
| "kept_ids = sorted(new_tokens)" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "1000\n", | |
| "12574\n", | |
| "23490\n", | |
| "23590\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I alt tilføjes der 23.590 tokens" | |
| ], | |
| "metadata": { | |
| "id": "tg62Ir3MIA9D" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "q21bC7tpTyuW", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2a64e930-64a0-4aa0-ca8a-978c4c32d40b" | |
| }, | |
| "source": [ | |
| "len(kept_ids) / tokenizer.vocab_size" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.09432227109156338" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BLAFLhrDoD4U" | |
| }, | |
| "source": [ | |
| "Det nye vocab indeholder kun 9% af det originale - hvilket giver mening, eftersom dansk er et lille sprog i en multilingual model." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "IaaCyAPlomLt" | |
| }, | |
| "source": [ | |
| "### Update model embeddings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "k-BNn3R6R0lY" | |
| }, | |
| "source": [ | |
| "import torch\n", | |
| "\n", | |
| "new_size = len(kept_ids)\n", | |
| "new_emb = torch.nn.Embedding(new_size, model.shared.embedding_dim)\n", | |
| "new_head = torch.nn.Linear(in_features=model.lm_head.in_features, out_features=new_size, bias=False)\n", | |
| "\n", | |
| "for new_id, old_id in enumerate(kept_ids):\n", | |
| " new_emb.weight.data[new_id] = model.shared.weight.data[old_id]\n", | |
| " new_head.weight.data[new_id] = model.lm_head.weight.data[old_id]\n", | |
| "\n", | |
| "model.shared.weight = new_emb.weight\n", | |
| "model.lm_head.weight = new_head.weight" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "g_aPBQ20kvCB", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "753f0987-ebc8-4824-a01c-73c1e51a07ac" | |
| }, | |
| "source": [ | |
| "print('Gammel model:')\n", | |
| "print(original_size)\n", | |
| "\n", | |
| "print('\\nNy model:')\n", | |
| "print(msize(model))\n", | |
| "print(msize(model.shared))\n", | |
| "\n", | |
| "print('\\nEncoder:')\n", | |
| "print(msize(model.encoder))\n", | |
| "print(msize(model.encoder.block))\n", | |
| "\n", | |
| "print('\\nDecoder:')\n", | |
| "print(msize(model.decoder))\n", | |
| "print(msize(model.decoder.block))\n", | |
| "print(msize(model.lm_head))\n", | |
| "\n", | |
| "print('\\nSammenligning med gammel model:')\n", | |
| "print(msize(model), msize(model) / original_size)" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Gammel model:\n", | |
| "582401280\n", | |
| "\n", | |
| "Ny model:\n", | |
| "234463488\n", | |
| "18117120\n", | |
| "\n", | |
| "Encoder:\n", | |
| "103071360\n", | |
| "84953472\n", | |
| "\n", | |
| "Decoder:\n", | |
| "131392128\n", | |
| "113274240\n", | |
| "18117120\n", | |
| "\n", | |
| "Sammenligning med gammel model:\n", | |
| "234463488 0.40258065366889306\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QcIDtmymo56s" | |
| }, | |
| "source": [ | |
| "Den nye model har 'kun' 234M parameter - 40% af den originale størrelse (der var på 582k). " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(msize(model.shared) / msize(model))\n", | |
| "print(msize(model.lm_head) / msize(model))" | |
| ], | |
| "metadata": { | |
| "id": "JJySfhScMNfh", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "19defc3e-56fe-480e-c98a-acda4249d650" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.07727053860087589\n", | |
| "0.07727053860087589\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Input og output embeddings udgør nu kun 14% af modellen (mod 66% før)" | |
| ], | |
| "metadata": { | |
| "id": "BZIdz0IiMfkV" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "vdKmFJY_k7xZ" | |
| }, | |
| "source": [ | |
| "### Update the tokenizer" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Her sker der præcis det samme som i den org. notebook" | |
| ], | |
| "metadata": { | |
| "id": "Vi9Q_m3BNHTM" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-X25sG0jmc83" | |
| }, | |
| "source": [ | |
| "T5 uses Sentencepiece tokenizer, which is implemented in C and is opaque to Python. \n", | |
| "\n", | |
| "Fortunately, we can download its model and deploy it into Python using its Protobuf representation. \n", | |
| "\n", | |
| "https://github.com/google/sentencepiece/issues/121" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "OpII_eX3mY80", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "1d927c52-cf5f-45cf-891b-00789ce11eec" | |
| }, | |
| "source": [ | |
| "!wget https://raw.githubusercontent.com/google/sentencepiece/master/src/sentencepiece_model.proto" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-01-06 10:12:52-- https://raw.githubusercontent.com/google/sentencepiece/master/src/sentencepiece_model.proto\n", | |
| "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n", | |
| "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 12872 (13K) [text/plain]\n", | |
| "Saving to: ‘sentencepiece_model.proto’\n", | |
| "\n", | |
| "\r sentencep 0%[ ] 0 --.-KB/s \rsentencepiece_model 100%[===================>] 12.57K --.-KB/s in 0s \n", | |
| "\n", | |
| "2022-01-06 10:12:52 (75.2 MB/s) - ‘sentencepiece_model.proto’ saved [12872/12872]\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SGb1DiYmpnkr" | |
| }, | |
| "source": [ | |
| "We compile the protobuf description of the sentencepiece model in order to be able to modify it. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "I6B0MA5DmaZM" | |
| }, | |
| "source": [ | |
| "!protoc --python_out=. sentencepiece_model.proto" | |
| ], | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "nJwHRRzbngJY" | |
| }, | |
| "source": [ | |
| "Now we can serialize the model used by the current tokenizer and open it as a protobuf class. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "MdQM0L3lnybA", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 101, | |
| "referenced_widgets": [ | |
| "9afa3fd99f634eca8ba8980fb950b9af", | |
| "5742a9b2a6ea4519bef24cef9afcbc1a", | |
| "36c1ac8da0f7462db9520d6d23eddae1", | |
| "04cd8227f7444ca6b6b8239d52bbe59e", | |
| "caaeb10b148e4777aba09bcc8befbc3c", | |
| "19acc50e25684b9481e6f7157239b35b", | |
| "73a2f37bdf534d2bb2f241f8e688fd7e", | |
| "56e9f9034fea48ebb267e458b2968d8b", | |
| "212f300fd08c4b9992f382474f72d31a", | |
| "ec774f016437475cba927a28515ce44f", | |
| "5365e04683f34410aadff091c2da6c96" | |
| ] | |
| }, | |
| "outputId": "8aeef595-2616-4c7f-a3d5-5d6efb6c95f7" | |
| }, | |
| "source": [ | |
| "import sentencepiece_model_pb2 as spmp\n", | |
| "smp = tokenizer.sp_model.serialized_model_proto()\n", | |
| "m = spmp.ModelProto()\n", | |
| "m.ParseFromString(smp)\n", | |
| "\n", | |
| "print('the loaded model has pieces:', len(m.pieces))\n", | |
| "new_pieces = [m.pieces[idx] for idx in kept_ids]\n", | |
| "print('the new pieces:', len(new_pieces))\n", | |
| "\n", | |
| "# replace the content of the first 30K pieces\n", | |
| "for i, p in enumerate(new_pieces):\n", | |
| " m.pieces[i].piece = p.piece\n", | |
| " m.pieces[i].score = p.score\n", | |
| " m.pieces[i].type = p.type\n", | |
| "\n", | |
| "# drop the remaining pieces\n", | |
| "n = len(new_pieces)\n", | |
| "for i in trange(len(m.pieces) - n):\n", | |
| " m.pieces.pop(len(m.pieces) - 1)\n", | |
| "\n", | |
| "print(len(m.pieces))\n", | |
| "with open('new_sp.model', 'wb') as f:\n", | |
| " f.write(m.SerializeToString())" | |
| ], | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "the loaded model has pieces: 250100\n", | |
| "the new pieces: 23590\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "9afa3fd99f634eca8ba8980fb950b9af", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| " 0%| | 0/226510 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "23590\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "qWeP6N1sry93" | |
| }, | |
| "source": [ | |
| "new_tokenizer = MT5Tokenizer('new_sp.model', extra_ids=0)" | |
| ], | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "czfXG1IqsDT4" | |
| }, | |
| "source": [ | |
| "### Gem model og tokenizer" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Rekonfigurerer model config:" | |
| ], | |
| "metadata": { | |
| "id": "TFXXRij7ZMhy" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "oanCNPiIsCdU", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "1967fe0d-e40d-4a20-c3ba-5bbfb0a62c2e" | |
| }, | |
| "source": [ | |
| "model.config.__dict__['vocab_size'] = new_size\n", | |
| "model.config.__dict__['_name_or_path'] = output_name\n", | |
| "model.config" | |
| ], | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "MT5Config {\n", | |
| " \"_name_or_path\": \"emillykkejensen/daT5-base\",\n", | |
| " \"architectures\": [\n", | |
| " \"MT5ForConditionalGeneration\"\n", | |
| " ],\n", | |
| " \"d_ff\": 2048,\n", | |
| " \"d_kv\": 64,\n", | |
| " \"d_model\": 768,\n", | |
| " \"decoder_start_token_id\": 0,\n", | |
| " \"dropout_rate\": 0.1,\n", | |
| " \"eos_token_id\": 1,\n", | |
| " \"feed_forward_proj\": \"gated-gelu\",\n", | |
| " \"initializer_factor\": 1.0,\n", | |
| " \"is_encoder_decoder\": true,\n", | |
| " \"layer_norm_epsilon\": 1e-06,\n", | |
| " \"model_type\": \"mt5\",\n", | |
| " \"num_decoder_layers\": 12,\n", | |
| " \"num_heads\": 12,\n", | |
| " \"num_layers\": 12,\n", | |
| " \"output_past\": true,\n", | |
| " \"pad_token_id\": 0,\n", | |
| " \"relative_attention_num_buckets\": 32,\n", | |
| " \"tie_word_embeddings\": false,\n", | |
| " \"tokenizer_class\": \"T5Tokenizer\",\n", | |
| " \"transformers_version\": \"4.15.0\",\n", | |
| " \"use_cache\": true,\n", | |
| " \"vocab_size\": 23590\n", | |
| "}" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Gem den nye model og den nye tokenizer" | |
| ], | |
| "metadata": { | |
| "id": "ynH3IMvDZkMP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UaebisNqr4Mk" | |
| }, | |
| "source": [ | |
| "new_tokenizer.save_pretrained('new-model')\n", | |
| "model.save_pretrained('new-model')" | |
| ], | |
| "execution_count": 26, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "nIoB98_9r7VU", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "d6f1f4ff-a409-41d4-cb49-0297a36ff936" | |
| }, | |
| "source": [ | |
| "!ls new-model -alsh" | |
| ], | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "total 896M\n", | |
| "4.0K drwxr-xr-x 2 root root 4.0K Jan 6 10:13 .\n", | |
| "4.0K drwxr-xr-x 1 root root 4.0K Jan 6 10:13 ..\n", | |
| "4.0K -rw-r--r-- 1 root root 711 Jan 6 10:13 config.json\n", | |
| "895M -rw-r--r-- 1 root root 895M Jan 6 10:13 pytorch_model.bin\n", | |
| "4.0K -rw-r--r-- 1 root root 65 Jan 6 10:13 special_tokens_map.json\n", | |
| "636K -rw-r--r-- 1 root root 636K Jan 6 10:13 spiece.model\n", | |
| "4.0K -rw-r--r-- 1 root root 173 Jan 6 10:13 tokenizer_config.json\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5gFLD5dUs7gZ" | |
| }, | |
| "source": [ | |
| "Den nye model, fylder en hel del mindre end den originale mt5 - kun 595 Mb mod 2,17 Gb!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!zip -r /content/new-model.zip /content/new-model\n" | |
| ], | |
| "metadata": { | |
| "id": "oe0Otwl5KgMu", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "7d7ac73a-5afb-4e6f-e73d-8f968275f998" | |
| }, | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " adding: content/new-model/ (stored 0%)\n", | |
| " adding: content/new-model/special_tokens_map.json (deflated 34%)\n", | |
| " adding: content/new-model/tokenizer_config.json (deflated 32%)\n", | |
| " adding: content/new-model/config.json (deflated 45%)\n", | |
| " adding: content/new-model/pytorch_model.bin (deflated 53%)\n", | |
| " adding: content/new-model/spiece.model (deflated 51%)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Zip og gem, hvis det er det du er til :)" | |
| ], | |
| "metadata": { | |
| "id": "h-Sx_mHyKt5c" | |
| } | |
| } | |
| ] | |
| } |
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