{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "cjgz1WTcPWee" }, "source": [ "# Lab 3: Training Tokenizers\n", "\n", "![](../figs/deep_nlp/lab/tokenize.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the environment\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "%pip install --pre ekorpkit[tokenize]\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "BzxPwsOVPWef" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.utils.notebook:Google Colab not detected.\n", "INFO:ekorpkit.base:Set environment variable EKORPKIT_WORKSPACE_ROOT=/content/drive/MyDrive/workspace\n", "INFO:ekorpkit.base:Set environment variable EKORPKIT_PROJECT_DIR=/content/drive/MyDrive/workspace/projects/ekorpkit-book\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "version: 0.1.40.post0.dev51\n", "is colab? False\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:compose config with overrides: ['+project=default']\n", "INFO:ekorpkit.base:There are no arguments to initilize a config, using default config.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "project_dir: /content/drive/MyDrive/workspace/projects/ekorpkit-book\n" ] }, { "data": { "text/plain": [ "{'EKORPKIT_CONFIG_DIR': '/workspace/projects/ekorpkit-book/config',\n", " 'EKORPKIT_WORKSPACE_ROOT': '/content/drive/MyDrive/workspace',\n", " 'EKORPKIT_PROJECT': 'ekorpkit-book',\n", " 'EKORPKIT_PROJECT_DIR': '/content/drive/MyDrive/workspace/projects/ekorpkit-book',\n", " 'EKORPKIT_DATA_DIR': None,\n", " 'EKORPKIT_LOG_LEVEL': 'INFO',\n", " 'NUM_WORKERS': 230,\n", " 'KMP_DUPLICATE_LIB_OK': 'TRUE',\n", " 'CUDA_DEVICE_ORDER': None,\n", " 'CUDA_VISIBLE_DEVICES': None,\n", " 'WANDB_PROJECT': None,\n", " 'WANDB_DISABLED': None,\n", " 'LABEL_STUDIO_SERVER': 'http://ekorpkit-labelstudio:8080',\n", " 'CACHED_PATH_CACHE_ROOT': None}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 936 ms (started: 2022-12-13 04:56:08 +00:00)\n" ] } ], "source": [ "from ekorpkit import eKonf\n", "\n", "eKonf.setLogger(\"INFO\")\n", "print(\"version:\", eKonf.__version__)\n", "\n", "is_colab = eKonf.is_colab()\n", "print(\"is colab?\", is_colab)\n", "if is_colab:\n", " eKonf.mount_google_drive()\n", "workspace_dir = \"/content/drive/MyDrive/workspace\"\n", "project_name = \"ekorpkit-book\"\n", "ws = eKonf.set_workspace(workspace=workspace_dir, project=project_name)\n", "print(\"project_dir:\", ws.project_dir)\n", "ws.envs.dict()" ] }, { "cell_type": "markdown", "metadata": { "id": "it1Y0MVPXRsJ" }, "source": [ "### Load the saved corpora\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.io.file:Processing [1] files from ['enko_filtered.parquet']\n", "INFO:ekorpkit.io.file:Loading 1 dataframes from ['/content/drive/MyDrive/workspace/projects/ekorpkit-book/data/enko_filtered.parquet']\n", "INFO:ekorpkit.io.file:Loading data from /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/enko_filtered.parquet\n" ] }, { "data": { "text/html": [ "
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idtextsplitfilenamecorpusnum_charsnum_wordsnum_sentsavg_num_charsavg_num_words
17644961Anaissini is a tribe of click beetles in the f...trainwiki_49enwiki_sampled631115.72727311.000000
26658552The Vicky Metcalf Award for Literature for You...trainwiki_24enwiki_sampled4798255.84146316.400000
411081255Eylex Films Pvt is a chain of multiplex and si...trainwiki_94enwiki_sampled1161181126.41436515.083333
84706486Željko Zečević (; born 21 October 1963) is a S...trainwiki_02enwiki_sampled1151201155.72636813.400000
122170359Gilberto Nascimento Silva (born 9 June 1956) i...trainwiki_57enwiki_sampled68510596.52381011.666667
\n", "
" ], "text/plain": [ " id text split \\\n", "1 7644961 Anaissini is a tribe of click beetles in the f... train \n", "2 6658552 The Vicky Metcalf Award for Literature for You... train \n", "4 11081255 Eylex Films Pvt is a chain of multiplex and si... train \n", "8 4706486 Željko Zečević (; born 21 October 1963) is a S... train \n", "12 2170359 Gilberto Nascimento Silva (born 9 June 1956) i... train \n", "\n", " filename corpus num_chars num_words num_sents avg_num_chars \\\n", "1 wiki_49 enwiki_sampled 63 11 1 5.727273 \n", "2 wiki_24 enwiki_sampled 479 82 5 5.841463 \n", "4 wiki_94 enwiki_sampled 1161 181 12 6.414365 \n", "8 wiki_02 enwiki_sampled 1151 201 15 5.726368 \n", "12 wiki_57 enwiki_sampled 685 105 9 6.523810 \n", "\n", " avg_num_words \n", "1 11.000000 \n", "2 16.400000 \n", "4 15.083333 \n", "8 13.400000 \n", "12 11.666667 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 3.17 s (started: 2022-12-13 00:32:31 +00:00)\n" ] } ], "source": [ "data = eKonf.load_data(\"enko_filtered.parquet\", ws.project_dir / \"data\")\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['The Rijeka Thermal Power Station (, TE Rijeka, also known as \"TE Urinj\") is an oil-fired power station east of Rijeka at Kostrena, Croatia. It was built between 1974 and 1978 and it has one generation unit with capacity of 320\\xa0MW. The height of the boiler house including its rooftop flue gas stack is .', 'Turbine for the power station was supplied by Ansaldo Energia. Ansaldo Energia was also awarded engineering, procurement and construction contract. Boilers were supplied by Waagner-Biro.', 'The power station is owned and operated by Hrvatska elektroprivreda. Its annual production varies, averaging 917 GWh, but only 141 GWh in 2011. It is expected to undergo decommissioning in 2020, but it is doubtful that it will remain operational until then because of its pollution problem. , Rijeka Thermal Power Station is offline, ready to resume generation within 160 hours of notice. On 18 October 2022, it was unofficially reported that HEP plans to restart the operation of the power plant in order to cover the losses incurred during the energy crisis.']\n", "['런던의 2012년 하계 올림픽 펜싱 여자 플뢰레 단체전은 8월 2일에 엑셀 박람회관에서 진행되었다.', '토너먼트 형식.', '9팀이 여자 플뢰레 단체전에 참가하였다. 본선에 출전하는 팀들은 FIE 랭킹에 따라 대진이 정해졌다. 영국은 개최국 자격으로 모든 종목을 선택 여부에 따라 참전할 수 있는 자격이 주어졌다. 영국은 이 토너먼트에 참가하여 첫경기에서 9번 시드의 이집트를 상대하여 승리하며 8강전에서 나머지 7개의 참가팀들과 만났다. 8강전에서 패한 팀들도 두 경기를 더 치러 5위에서 8위까지의 순위를 결정한다. 반대로, 8강전에서 승리한 팀들은 준결승전에서 서로와 맞붙는다. 준결승전에서 승리한 두 팀은 금메달 결정전으로, 패한 팀들은 동메달 결정전으로 이동한다.', '단체전은 먼저 45투셰를 기록하거나, 정규시간이 지나고 더 많은 투셰를 기록한 팀이 승리한다.']\n", "time: 145 ms (started: 2022-11-14 02:09:16 +00:00)\n" ] } ], "source": [ "text_column = \"text\"\n", "\n", "text_en = (\n", " data[data.corpus == \"enwiki_sampled\"][text_column].sample(1).values[0].split(\"\\n\")\n", ")\n", "text_ko = data[data.corpus == \"kowiki\"][text_column].sample(1).values[0].split(\"\\n\")\n", "\n", "print(text_en)\n", "print(text_ko)\n" ] }, { "cell_type": "markdown", "metadata": { "notebookRunGroups": { "groupValue": "1" } }, "source": [ "### Covert pandas datafame to huggingface dataset\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['text', '__index_level_0__'],\n", " num_rows: 603719\n", "})" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 1.22 s (started: 2022-11-14 02:09:19 +00:00)\n" ] } ], "source": [ "from datasets import Dataset\n", "\n", "raw_dataset = Dataset.from_pandas(data[[text_column]])\n", "raw_dataset\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shuffle the dataset\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 116 ms (started: 2022-11-14 02:09:21 +00:00)\n" ] } ], "source": [ "# shuffle the dataset\n", "\n", "raw_dataset = raw_dataset.shuffle(seed=42)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split the dataset into sentences for training\n", "\n", "The sentencepiece module comes with a python training API, which uses sentences in a file, one sentence per line. We will use the `sent_tokenize` function from the `nltk` package to split the text into sentences. The `sent_tokenize` function is a wrapper around the `punkt` tokenizer, which is a pre-trained sentence tokenizer. The `punkt` tokenizer is trained on the Penn Treebank corpus, which is a collection of Wall Street Journal articles. The `punkt` tokenizer is a good choice for plain English text, but it may not be the best choice for other languages.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 1.13 s (started: 2022-11-14 02:09:23 +00:00)\n" ] } ], "source": [ "import nltk\n", "from nltk.tokenize import sent_tokenize\n", "from ekorpkit.tokenizers.trainers.spm import export_sentence_chunk_files\n", "\n", "nltk.download(\"punkt\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false, "tags": [ "hide-output" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.tokenizers.trainers.spm:Writing sentence chunks to /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/tokenizers/enko_filtered_chunk\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "97b44995833848fe87bdb05c71aa4982", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/61 [00:00\" # token for unknown words\n", "spl_tokens = [\"\", \"\", \"\", \"\", \"[MASK]\"] # special tokens\n", "\n", "\n", "def prepare_tokenizer_trainer(algo):\n", " \"\"\"\n", " Prepares the tokenizer and trainer with unknown & special tokens.\n", " \"\"\"\n", " if algo == \"BPE\":\n", " tokenizer = Tokenizer(BPE(unk_token=unk_token))\n", " trainer = BpeTrainer(special_tokens=spl_tokens)\n", " elif algo == \"UNI\":\n", " tokenizer = Tokenizer(Unigram())\n", " trainer = UnigramTrainer(unk_token=unk_token, special_tokens=spl_tokens)\n", " else:\n", " tokenizer = Tokenizer(WordLevel(unk_token=unk_token))\n", " trainer = WordLevelTrainer(special_tokens=spl_tokens)\n", "\n", " normalizer = normalizers.Sequence([NFD(), StripAccents()])\n", " tokenizer.normalizer = normalizer\n", " tokenizer.pre_tokenizer = Whitespace()\n", "\n", " return tokenizer, trainer\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 20.2 ms (started: 2022-11-13 03:09:51 +00:00)\n" ] } ], "source": [ "def train_tokenizer(algo=\"BPE\"):\n", " \"\"\"\n", " Takes the files and trains the tokenizer.\n", " \"\"\"\n", " save_path = f\"{project_dir}/tokenizers/{algo}_tokenizer.json\"\n", " tokenizer, trainer = prepare_tokenizer_trainer(algo)\n", " tokenizer.train_from_iterator(\n", " batch_chunks(raw_dataset, batch_size=1000, text_column=text_column),\n", " trainer=trainer,\n", " )\n", " tokenizer.save(save_path)\n", " tokenizer = Tokenizer.from_file(save_path)\n", " return tokenizer\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train BPE tokenizer\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_path = train_tokenizer(\"BPE\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> time: 15h 30min 58s (started: 2022-11-11 06:36:01 +00:00)\n", "\n", "To train a BPE tokenizer, it took 15 hours and 30 minutes for the 2,998,856 sentences. The tokenizer was saved in the `{project_dir}/tokenizers` directory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train more efficiently with multiple processors, it is preferable to use CLI (command line interface) tools.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "ekorpkit \\\n", " project.name=ekorpkit-book \\\n", " dir.workspace=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer \\\n", " tokenizer.name=enkowiki \\\n", " tokenizer.dataset.train_file=enko_filtered.parquet \\\n", " tokenizer.model.model_type=bpe \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.model.character_coverage=0.9995 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.auto=train\n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> took 22m 36.6s \n", "\n", "With 256 processors, it took 22 minutes and 37 seconds to tokenize the 2,998,856 sentences.\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab size: 30000\n", "['This', 'article', 'describ', 'es', 'the', 'process', 'by', 'which', 'the', 'ter', 'rit', 'or', 'ial', 'ext', 'ent', 'of', 'Moroc', 'co', 'came', 'to', 'be', 'as', 'it', 'is', 'now', '.']\n", "['크리', '클', '레이드', '(', 'C', 'rick', 'l', 'ade', ')', '는', '잉글랜드의', '노스', '윌', '트', '셔', '에', '위치한', '템', '스', '강의', '타운', '이자', '지방', '행정', '구이다', '.', '스', '윈', '던', '과', '시', '런', '세', '스터', '의', '중간에', '위치해', '있다', '.']\n", "time: 67.4 ms (started: 2022-11-13 08:47:47 +00:00)\n" ] } ], "source": [ "from tokenizers import Tokenizer\n", "\n", "tokenizer_path = (\n", " f\"{project_dir}/tokenizers/hf/enko_wiki/enko_wiki_bpe_huggingface_vocab_30000.json\"\n", ")\n", "\n", "bpe_tokenizer = Tokenizer.from_file(tokenizer_path)\n", "print(f\"Vocab size: {bpe_tokenizer.get_vocab_size()}\")\n", "print(bpe_tokenizer.encode(text_en[0]).tokens)\n", "print(bpe_tokenizer.encode(text_ko[0]).tokens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Unigram tokenizer\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "model_path = train_tokenizer(\"UNI\")\n", "```\n", "\n", "For a very large corpus, it may take a long time to train a Unigram tokenizer. It is recommended to use the following CLI command to train a Unigram tokenizer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "ekorpkit \\\n", " project.name=ekorpkit-book \\\n", " dir.workspace=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer \\\n", " tokenizer.name=enkowiki \\\n", " tokenizer.dataset.train_file=enko_filtered.parquet \\\n", " tokenizer.model.model_type=unigram \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> took 10m 10.7s\n", "\n", "With 256 processors, it took 10 minutes and 11 seconds to tokenize the 2,998,856 sentences.\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab size: 30000\n", "['This', 'article', 'describe', 's', 'the', 'process', 'by', 'which', 'the', 'territor', 'ial', 'ex', 't', 'ent', 'of', 'Morocc', 'o', 'came', 'to', 'be', 'as', 'it', 'is', 'now', '.']\n", "['크리', '클레이', '드', '(', 'C', 'rick', 'la', 'de', ')', '는', '잉글랜드의', '노스', '윌', '트', '셔', '에', '위치한', '템', '스', '강의', '타운', '이자', '지방', '행정', '구', '이다', '.', '스', '윈', '던', '과', '시', '런', '세', '스터', '의', '중간에', '위치해', '있다', '.']\n", "time: 82 ms (started: 2022-11-13 08:47:51 +00:00)\n" ] } ], "source": [ "from tokenizers import Tokenizer\n", "\n", "tokenizer_path = (\n", " f\"{project_dir}/tokenizers/hf/enko_wiki/enko_wiki_unigram_huggingface_vocab_30000.json\"\n", ")\n", "\n", "unigram_tokenizer = Tokenizer.from_file(tokenizer_path)\n", "print(f\"Vocab size: {bpe_tokenizer.get_vocab_size()}\")\n", "print(unigram_tokenizer.encode(text_en[0]).tokens)\n", "print(unigram_tokenizer.encode(text_ko[0]).tokens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train tokenizers with Google SentencePiece (SPM)\n", "\n", "### Install SentencePiece\n", "\n", "```bash\n", "pip install sentencepiece\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train SentencePiece models\n", "\n", "You can use `train_spm` function to train a SentencePiece model. The `train_spm` function takes the following arguments:\n", "\n", "- `model_prefix`: The prefix of the model file. The model file will be saved as `{model_prefix}_{model_type}_vocab_{vocab_size}.model`.\n", "- `input`: The input file for training.\n", "- `output_dir`: The directory to save the model file.\n", "- `vocab_size`: The vocabulary size.\n", "- `model_type`: The model type. It can be `unigram` (default), `bpe`, `char`, or `word`.\n", "- `character_coverage`: The character coverage. It is only used for `unigram` and `bpe` model types. The default value is `1.0`.\n", "- `num_threads`: The number of threads to use for training. The default value is `1`. The max value is `128`.\n", "- `train_extremely_large_corpus`: Whether to train an extremely large corpus. The default value is `False`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train Unigram model\n" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": false, "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 20.3 ms (started: 2022-11-25 02:01:41 +00:00)\n" ] } ], "source": [ "from ekorpkit.tokenizers.trainers.spm import train_spm\n", "\n", "uni_model_path = train_spm(\n", " model_prefix=\"enko_wiki\",\n", " input=sampled_file,\n", " output_dir=project_dir + \"/tokenizers/spm\",\n", " model_type=\"unigram\",\n", " vocab_size=30000,\n", " character_coverage=0.9995,\n", " num_threads=128,\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> time: 23min 6s (started: 2022-11-12 08:03:08 +00:00)\n", "\n", "It took 23 minutes to train a unigram model with a vocabulary size of 30,000. The model file was saved in the `{project_dir}/tokenizers` directory.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "ekorpkit \\\n", " project.name=ekorpkit-book \\\n", " dir.workspace=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer.spm \\\n", " tokenizer.name=enkowiki \\\n", " tokenizer.dataset.train_file=enko_filtered.parquet \\\n", " tokenizer.model.model_type=unigram \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.model.character_coverage=0.9995 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> took 4m 2.4s\n", "\n", "With 128 processors, it took 4 minutes and 2 seconds to tokenize the 2,998,856 sentences.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load the trained model\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab size: 30000\n", "['▁This', '▁article', '▁describes', '▁the', '▁process', '▁by', '▁which', '▁the', '▁', 'ter', 'ri', 'torial', '▁ex', 't', 'ent', '▁of', '▁Morocco', '▁came', '▁to', '▁be', '▁as', '▁it', '▁is', '▁now', '.']\n", "['▁크리', '클', '레이드', '(', 'C', 'rick', 'lade', ')', '는', '▁잉글랜드의', '▁노스', '▁윌', '트', '셔', '에', '▁위치한', '▁', '템', '스', '▁강의', '▁타운', '이자', '▁지방', '▁행정', '구', '이다', '.', '▁스', '윈', '던', '과', '▁시', '런', '세', '스터', '의', '▁중간에', '▁위치해', '▁있다', '.']\n", "time: 67.4 ms (started: 2022-11-13 08:48:00 +00:00)\n" ] } ], "source": [ "import sentencepiece as spm\n", "\n", "model_file = \"tokenizers/spm/enko_wiki_unigram_vocab_30000.model\"\n", "model_file = project_dir + \"/\" + model_file\n", "uni_spm = spm.SentencePieceProcessor(model_file=model_file)\n", "print(f\"Vocab size: {uni_spm.vocab_size()}\")\n", "print(uni_spm.encode(text_en[0], out_type=str))\n", "print(uni_spm.encode(text_ko[0], out_type=str))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train BPE model\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "ekorpkit \\\n", " project.name=ekorpkit-book \\\n", " dir.workspace=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer.spm \\\n", " tokenizer.name=enkowiki \\\n", " tokenizer.dataset.train_file=enko_filtered.parquet \\\n", " tokenizer.model.model_type=bpe \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.model.character_coverage=0.9995 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> took 8m 41.8s\n", "\n", "With 128 processors, it took 8 minutes and 42 seconds to tokenize the 2,998,856 sentences.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load the trained model\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab size: 30000\n", "['▁This', '▁article', '▁describes', '▁the', '▁process', '▁by', '▁which', '▁the', '▁ter', 'rit', 'orial', '▁ext', 'ent', '▁of', '▁Morocco', '▁came', '▁to', '▁be', '▁as', '▁it', '▁is', '▁now', '.']\n", "['▁크리', '클', '레이드', '(', 'C', 'rick', 'l', 'ade', ')', '는', '▁잉글랜드의', '▁노스', '▁윌', '트', '셔', '에', '▁위치한', '▁템', '스', '▁강의', '▁타운', '이자', '▁지방', '▁행정', '구이다', '.', '▁스', '윈', '던', '과', '▁시', '런', '세', '스터', '의', '▁중간에', '▁위치해', '▁있다', '.']\n", "time: 34.1 ms (started: 2022-11-13 08:48:11 +00:00)\n" ] } ], "source": [ "import sentencepiece as spm\n", "\n", "model_file = \"tokenizers/spm/enko_wiki_bpe_vocab_30000.model\"\n", "model_file = project_dir + \"/\" + model_file\n", "bpe_spm = spm.SentencePieceProcessor(model_file=model_file)\n", "print(f\"Vocab size: {uni_spm.vocab_size()}\")\n", "print(bpe_spm.encode(text_en[0], out_type=str))\n", "print(bpe_spm.encode(text_ko[0], out_type=str))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare the Tokenizers\n", "\n", "### Load the tokenizers\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 20.6 ms (started: 2022-11-13 08:48:24 +00:00)\n" ] } ], "source": [ "tokenizers = {\n", " \"BPE\": bpe_tokenizer,\n", " \"UNI\": unigram_tokenizer,\n", " \"UNI_SPM\": uni_spm,\n", " \"BPE_SPM\": bpe_spm,\n", "}\n", "\n", "\n", "def tokenize(tokenizer, text):\n", " \"\"\"\n", " Tokenizes the text using the tokenizer.\n", " \"\"\"\n", " if isinstance(tokenizer, spm.SentencePieceProcessor):\n", " return tokenizer.encode(text, out_type=str)\n", " return tokenizer.encode(text).tokens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyze the output of the tokenizers\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tokenizer: BPE\n", "['This', 'article', 'describ', 'es', 'the', 'process', 'by', 'which', 'the', 'ter', 'rit', 'or', 'ial', 'ext', 'ent', 'of', 'Moroc', 'co', 'came', 'to', 'be', 'as', 'it', 'is', 'now', '.']\n", "--------------------------------------------------\n", "Tokenizer: UNI\n", "['This', 'article', 'describe', 's', 'the', 'process', 'by', 'which', 'the', 'territor', 'ial', 'ex', 't', 'ent', 'of', 'Morocc', 'o', 'came', 'to', 'be', 'as', 'it', 'is', 'now', '.']\n", "--------------------------------------------------\n", "Tokenizer: UNI_SPM\n", "['▁This', '▁article', '▁describes', '▁the', '▁process', '▁by', '▁which', '▁the', '▁', 'ter', 'ri', 'torial', '▁ex', 't', 'ent', '▁of', '▁Morocco', '▁came', '▁to', '▁be', '▁as', '▁it', '▁is', '▁now', '.']\n", "--------------------------------------------------\n", "Tokenizer: BPE_SPM\n", "['▁This', '▁article', '▁describes', '▁the', '▁process', '▁by', '▁which', '▁the', '▁ter', 'rit', 'orial', '▁ext', 'ent', '▁of', '▁Morocco', '▁came', '▁to', '▁be', '▁as', '▁it', '▁is', '▁now', '.']\n", "--------------------------------------------------\n", "Tokenizer: BPE\n", "['크리', '클', '레이드', '(', 'C', 'rick', 'l', 'ade', ')', '는', '잉글랜드의', '노스', '윌', '트', '셔', '에', '위치한', '템', '스', '강의', '타운', '이자', '지방', '행정', '구이다', '.', '스', '윈', '던', '과', '시', '런', '세', '스터', '의', '중간에', '위치해', '있다', '.']\n", "--------------------------------------------------\n", "Tokenizer: UNI\n", "['크리', '클레이', '드', '(', 'C', 'rick', 'la', 'de', ')', '는', '잉글랜드의', '노스', '윌', '트', '셔', '에', '위치한', '템', '스', '강의', '타운', '이자', '지방', '행정', '구', '이다', '.', '스', '윈', '던', '과', '시', '런', '세', '스터', '의', '중간에', '위치해', '있다', '.']\n", "--------------------------------------------------\n", "Tokenizer: UNI_SPM\n", "['▁크리', '클', '레이드', '(', 'C', 'rick', 'lade', ')', '는', '▁잉글랜드의', '▁노스', '▁윌', '트', '셔', '에', '▁위치한', '▁', '템', '스', '▁강의', '▁타운', '이자', '▁지방', '▁행정', '구', '이다', '.', '▁스', '윈', '던', '과', '▁시', '런', '세', '스터', '의', '▁중간에', '▁위치해', '▁있다', '.']\n", "--------------------------------------------------\n", "Tokenizer: BPE_SPM\n", "['▁크리', '클', '레이드', '(', 'C', 'rick', 'l', 'ade', ')', '는', '▁잉글랜드의', '▁노스', '▁윌', '트', '셔', '에', '▁위치한', '▁템', '스', '▁강의', '▁타운', '이자', '▁지방', '▁행정', '구이다', '.', '▁스', '윈', '던', '과', '▁시', '런', '세', '스터', '의', '▁중간에', '▁위치해', '▁있다', '.']\n", "--------------------------------------------------\n", "time: 20.8 ms (started: 2022-11-13 08:48:25 +00:00)\n" ] } ], "source": [ "texts = [text_en[0] , text_ko[0]]\n", "tokens = {name: [] for name in tokenizers.keys()}\n", "\n", "\n", "# tokenize the texts with the tokenizers\n", "for text in texts:\n", " for name, tokenizer in tokenizers.items():\n", " print(f\"Tokenizer: {name}\")\n", " tokens[name].append(tokenize(tokenizer, text))\n", " print(tokens[name][-1])\n", " print(\"-\" * 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare the Tokens\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 20.5 ms (started: 2022-11-13 08:48:30 +00:00)\n" ] } ], "source": [ "import pandas as pd\n", "\n", "\n", "def compare_tokens(tokenizers, tokens, sample_num=0):\n", "\n", " max_len = max(len(tokens[name][sample_num]) for name in tokenizers.keys())\n", " diffs = {\n", " name: max_len - len(tokens[name][sample_num]) for name in tokenizers.keys()\n", " }\n", "\n", " padded_tokens = {\n", " name: tokens[name][sample_num] + [\"\"] * diffs[name]\n", " for name in tokenizers.keys()\n", " }\n", "\n", " df = pd.DataFrame(padded_tokens)\n", " return df" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BPEUNIUNI_SPMBPE_SPM
0ThisThis▁This▁This
1articlearticle▁article▁article
2describdescribe▁describes▁describes
3ess▁the▁the
4thethe▁process▁process
5processprocess▁by▁by
6byby▁which▁which
7whichwhich▁the▁the
8thethe▁▁ter
9terterritorterrit
10ritialriorial
11orextorial▁ext
12ialt▁exent
13extentt▁of
14entofent▁Morocco
15ofMorocc▁of▁came
16Moroco▁Morocco▁to
17cocame▁came▁be
18cameto▁to▁as
19tobe▁be▁it
20beas▁as▁is
21asit▁it▁now
22itis▁is.
23isnow▁now
24now..
25.
\n", "
" ], "text/plain": [ " BPE UNI UNI_SPM BPE_SPM\n", "0 This This ▁This ▁This\n", "1 article article ▁article ▁article\n", "2 describ describe ▁describes ▁describes\n", "3 es s ▁the ▁the\n", "4 the the ▁process ▁process\n", "5 process process ▁by ▁by\n", "6 by by ▁which ▁which\n", "7 which which ▁the ▁the\n", "8 the the ▁ ▁ter\n", "9 ter territor ter rit\n", "10 rit ial ri orial\n", "11 or ex torial ▁ext\n", "12 ial t ▁ex ent\n", "13 ext ent t ▁of\n", "14 ent of ent ▁Morocco\n", "15 of Morocc ▁of ▁came\n", "16 Moroc o ▁Morocco ▁to\n", "17 co came ▁came ▁be\n", "18 came to ▁to ▁as\n", "19 to be ▁be ▁it\n", "20 be as ▁as ▁is\n", "21 as it ▁it ▁now\n", "22 it is ▁is .\n", "23 is now ▁now \n", "24 now . . \n", "25 . " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 26.6 ms (started: 2022-11-13 08:48:31 +00:00)\n" ] } ], "source": [ "compare_tokens(tokenizers, tokens, sample_num=0)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BPEUNIUNI_SPMBPE_SPM
0크리크리▁크리▁크리
1클클레이클클
2레이드드레이드레이드
3((((
4CCCC
5rickrickrickrick
6llaladel
7adede)ade
8))는)
9는는▁잉글랜드의는
10잉글랜드의잉글랜드의▁노스▁잉글랜드의
11노스노스▁윌▁노스
12윌윌트▁윌
13트트셔트
14셔셔에셔
15에에▁위치한에
16위치한위치한▁▁위치한
17템템템▁템
18스스스스
19강의강의▁강의▁강의
20타운타운▁타운▁타운
21이자이자이자이자
22지방지방▁지방▁지방
23행정행정▁행정▁행정
24구이다구구구이다
25.이다이다.
26스..▁스
27윈스▁스윈
28던윈윈던
29과던던과
30시과과▁시
31런시▁시런
32세런런세
33스터세세스터
34의스터스터의
35중간에의의▁중간에
36위치해중간에▁중간에▁위치해
37있다위치해▁위치해▁있다
38.있다▁있다.
39..
\n", "
" ], "text/plain": [ " BPE UNI UNI_SPM BPE_SPM\n", "0 크리 크리 ▁크리 ▁크리\n", "1 클 클레이 클 클\n", "2 레이드 드 레이드 레이드\n", "3 ( ( ( (\n", "4 C C C C\n", "5 rick rick rick rick\n", "6 l la lade l\n", "7 ade de ) ade\n", "8 ) ) 는 )\n", "9 는 는 ▁잉글랜드의 는\n", "10 잉글랜드의 잉글랜드의 ▁노스 ▁잉글랜드의\n", "11 노스 노스 ▁윌 ▁노스\n", "12 윌 윌 트 ▁윌\n", "13 트 트 셔 트\n", "14 셔 셔 에 셔\n", "15 에 에 ▁위치한 에\n", "16 위치한 위치한 ▁ ▁위치한\n", "17 템 템 템 ▁템\n", "18 스 스 스 스\n", "19 강의 강의 ▁강의 ▁강의\n", "20 타운 타운 ▁타운 ▁타운\n", "21 이자 이자 이자 이자\n", "22 지방 지방 ▁지방 ▁지방\n", "23 행정 행정 ▁행정 ▁행정\n", "24 구이다 구 구 구이다\n", "25 . 이다 이다 .\n", "26 스 . . ▁스\n", "27 윈 스 ▁스 윈\n", "28 던 윈 윈 던\n", "29 과 던 던 과\n", "30 시 과 과 ▁시\n", "31 런 시 ▁시 런\n", "32 세 런 런 세\n", "33 스터 세 세 스터\n", "34 의 스터 스터 의\n", "35 중간에 의 의 ▁중간에\n", "36 위치해 중간에 ▁중간에 ▁위치해\n", "37 있다 위치해 ▁위치해 ▁있다\n", "38 . 있다 ▁있다 .\n", "39 . . " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 26.1 ms (started: 2022-11-13 08:48:32 +00:00)\n" ] } ], "source": [ "compare_tokens(tokenizers, tokens, sample_num=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage of Tokenizer Trainer" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.io.file:Processing [1] files from ['wiki_filtered.parquet']\n", "INFO:ekorpkit.io.file:Loading 1 dataframes from ['/content/drive/MyDrive/workspace/projects/ekorpkit-book/data/wiki_filtered.parquet']\n", "INFO:ekorpkit.io.file:Loading data from /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/wiki_filtered.parquet\n", "INFO:root:compose config with overrides: ['tokenizer=trainer']\n", "INFO:ekorpkit.io.file:Saving dataframe to /content/drive/MyDrive/workspace/projects/ekorpkit-book/language-modeling/data/bnwiki_filtered.parquet\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time: 16.5 s (started: 2022-11-25 02:14:06 +00:00)\n" ] } ], "source": [ "data = eKonf.load_data(\"wiki_filtered.parquet\", project_dir + \"/data\")\n", "bnwiki_filtered = data[data.corpus == \"bnwiki\"]\n", "\n", "data_file = \"bnwiki_filtered.parquet\"\n", "cfg = eKonf.compose(\"tokenizer=trainer\")\n", "cfg.name = \"bnwiki\"\n", "cfg.batch.num_workers = 128\n", "cfg.dataset.train_file = data_file\n", "cfg.training.use_sample = False\n", "cfg.model.model_type = \"bpe\"\n", "cfg.model.vocab_size = 30000\n", "cfg.auto = \"train\"\n", "\n", "eKonf.save_data(bnwiki_filtered, data_file, cfg.path.data_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BPE Model by Hugging Face\n", "\n", "```bash\n", "ekorpkit \\\n", " project_name=ekorpkit-book \\\n", " workspace_dir=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer \\\n", " tokenizer.name=bnwiki \\\n", " tokenizer.dataset.train_file=data/bnwiki_filtered.parquet \\\n", " tokenizer.model.model_type=bpe \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "```" ] }, { "cell_type": "markdown", "metadata": { "scrolled": true, "tags": [ "hide-output" ] }, "source": [ "### Unigram Model by Hugging Face\n", "\n", "\n", "```bash\n", "ekorpkit \\\n", " project_name=ekorpkit-book \\\n", " workspace_dir=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer \\\n", " tokenizer.name=bnwiki \\\n", " tokenizer.dataset.train_file=data/bnwiki_filtered.parquet \\\n", " tokenizer.model.model_type=unigram \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BPE Model by SentencePiece\n", "\n", "```bash\n", "ekorpkit \\\n", " project_name=ekorpkit-book \\\n", " workspace_dir=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer.spm \\\n", " tokenizer.name=bnwiki \\\n", " tokenizer.dataset.train_file=data/bnwiki_filtered.parquet \\\n", " tokenizer.model.model_type=bpe \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.model.character_coverage=0.9995 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unigram Model by SentencePiece\n", "\n", "```bash\n", "ekorpkit \\\n", " project_name=ekorpkit-book \\\n", " workspace_dir=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer.spm \\\n", " tokenizer.name=bnwiki \\\n", " tokenizer.dataset.train_file=data/bnwiki_filtered.parquet \\\n", " tokenizer.model.model_type=unigram \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer.model.character_coverage=0.9995 \\\n", " tokenizer.training.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare the Tokenizers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 16.2 ms (started: 2022-11-29 10:48:09 +00:00)\n" ] } ], "source": [ "from ekorpkit.tokenizers.trainer import TokenizerTrainer, compare_tokens\n", "\n", "\n", "def load_tokenizer(trainer_type, model_type, name=\"kowiki\"):\n", " cfg_group = \"tokenizer=trainer\"\n", " if trainer_type == \"spm\":\n", " cfg_group += \".spm\"\n", " cfg = eKonf.compose(cfg_group)\n", " cfg.name = name\n", " cfg.model.model_type = model_type\n", " trainer = TokenizerTrainer(**cfg)\n", " \n", " return trainer.tokenizer_obj" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:compose config with overrides: ['tokenizer=trainer.spm']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "bpe_spm\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.base:No method defined to call\n", "INFO:root:compose config with overrides: ['tokenizer=trainer.spm']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "unigram_spm\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.base:No method defined to call\n", "INFO:root:compose config with overrides: ['tokenizer=trainer']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "bpe_huggingface\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.base:No method defined to call\n", "INFO:root:compose config with overrides: ['tokenizer=trainer']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "unigram_huggingface\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.base:No method defined to call\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time: 2.36 s (started: 2022-11-29 10:48:14 +00:00)\n" ] } ], "source": [ "tokenizers = {}\n", "\n", "for trainer_type in ['spm', 'huggingface']:\n", " for model_type in ['bpe', 'unigram']:\n", " name = f\"{model_type}_{trainer_type}\"\n", " print(name)\n", " tokenizers[name] = load_tokenizer(trainer_type, model_type, \"bnwiki\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:compose config with overrides: ['tokenizer=trainer']\n", "INFO:ekorpkit.base:No method defined to call\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "number of sentences: 357833\n", "জোড্ডা পূর্ব বাংলাদেশের কুমিল্লা জেলার অন্তর্গত নাঙ্গলকোট উপজেলার একটি ইউনিয়ন।\n", "time: 980 ms (started: 2022-11-29 10:48:22 +00:00)\n" ] } ], "source": [ "cfg_group = \"tokenizer=trainer\"\n", "cfg = eKonf.compose(cfg_group)\n", "cfg.name = \"bnwiki\"\n", "trainer = TokenizerTrainer(**cfg)\n", "\n", "sentences = []\n", "files = trainer.input_files\n", "for file in files:\n", " with open(file, \"r\") as f:\n", " sentences.extend(f.readlines())\n", "\n", "print(\"number of sentences:\", len(sentences))\n", "print(sentences[0].strip())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Text: মোহাম্মদ রফিকুল ইসলাম ২০২০ সালের ১ সেপ্টেম্বর গাজীপুরে অবস্থিত আন্তর্জাতিক প্রযুক্তি বিশ্ববিদ্যালয়, ইসলামিক ইউনিভার্সিটি অব টেকনোলজির (আইইউটি) উপাচার্য হিসেবে যোগদান করেন।\n" ] }, { "data": { "text/html": [ "
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bpe_spmunigram_spmbpe_huggingfaceunigram_huggingface
0▁মোহাম্মদ▁মোহাম্মদমহমমদ▁
1▁রফিকুল▁রফিকুলরফকলমহমমদ
2▁ইসলাম▁ইসলামইসলম▁
3▁২০২০▁২০২০২০২০রফকল
4▁সালের▁সালেরসলর▁
5▁১▁১১ইসলম
6▁সেপ্টেম্বর▁সেপ্টেম্বরসপটমবর▁
7▁গাজীপুর▁গাজীপুরগজপর২০২০
8েেঅবসথত▁
9▁অবস্থিত▁অবস্থিতআনতরজতকসলর
10▁আন্তর্জাতিক▁আন্তর্জাতিকপরযকত▁
11▁প্রযুক্তি▁প্রযুক্তিবশববদযলয১
12▁বিশ্ববিদ্যালয়▁বিশ্ববিদ্যালয়,▁
13,,ইসলমকসপটমবর
14▁ইসলামিক▁ইসলামিকইউনভরসট▁
15▁ইউনিভার্সিটি▁ইউনিভার্সিটিঅবগজপর
16▁অব▁অবটকনলজর▁
17▁টেকনোলজির▁টেকনোলজির(অবসথত
18▁(▁(আই▁
19আইআইইউইউটআনতরজতক
20ইউটি)▁
21টি)উপচরযপরযকত
22)▁উপাচার্যহসব▁
23▁উপাচার্য▁হিসেবেযগদনবশববদযলয
24▁হিসেবে▁যোগদানকরন,
25▁যোগদান▁করেন।▁
26▁করেন।ইসলমক
27।▁
28ইউনভরসট
29▁
30অব
31▁
32টকনলজ
33র
34▁
35(
36আইইউ
37ট
38)
39▁
40উপচরয
41▁
42হসব
43▁
44যগদন
45▁
46করন
47।
\n", "
" ], "text/plain": [ " bpe_spm unigram_spm bpe_huggingface unigram_huggingface\n", "0 ▁মোহাম্মদ ▁মোহাম্মদ মহমমদ ▁\n", "1 ▁রফিকুল ▁রফিকুল রফকল মহমমদ\n", "2 ▁ইসলাম ▁ইসলাম ইসলম ▁\n", "3 ▁২০২০ ▁২০২০ ২০২০ রফকল\n", "4 ▁সালের ▁সালের সলর ▁\n", "5 ▁১ ▁১ ১ ইসলম\n", "6 ▁সেপ্টেম্বর ▁সেপ্টেম্বর সপটমবর ▁\n", "7 ▁গাজীপুর ▁গাজীপুর গজপর ২০২০\n", "8 ে ে অবসথত ▁\n", "9 ▁অবস্থিত ▁অবস্থিত আনতরজতক সলর\n", "10 ▁আন্তর্জাতিক ▁আন্তর্জাতিক পরযকত ▁\n", "11 ▁প্রযুক্তি ▁প্রযুক্তি বশববদযলয ১\n", "12 ▁বিশ্ববিদ্যালয় ▁বিশ্ববিদ্যালয় , ▁\n", "13 , , ইসলমক সপটমবর\n", "14 ▁ইসলামিক ▁ইসলামিক ইউনভরসট ▁\n", "15 ▁ইউনিভার্সিটি ▁ইউনিভার্সিটি অব গজপর\n", "16 ▁অব ▁অব টকনলজর ▁\n", "17 ▁টেকনোলজির ▁টেকনোলজির ( অবসথত\n", "18 ▁( ▁( আই ▁\n", "19 আই আইইউ ইউট আনতরজতক\n", "20 ইউ টি ) ▁\n", "21 টি ) উপচরয পরযকত\n", "22 ) ▁উপাচার্য হসব ▁\n", "23 ▁উপাচার্য ▁হিসেবে যগদন বশববদযলয\n", "24 ▁হিসেবে ▁যোগদান করন ,\n", "25 ▁যোগদান ▁করেন । ▁\n", "26 ▁করেন । ইসলমক\n", "27 । ▁\n", "28 ইউনভরসট\n", "29 ▁\n", "30 অব\n", "31 ▁\n", "32 টকনলজ\n", "33 র\n", "34 ▁\n", "35 (\n", "36 আইইউ\n", "37 ট\n", "38 )\n", "39 ▁\n", "40 উপচরয\n", "41 ▁\n", "42 হসব\n", "43 ▁\n", "44 যগদন\n", "45 ▁\n", "46 করন\n", "47 ।" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 25 ms (started: 2022-11-29 10:48:30 +00:00)\n" ] } ], "source": [ "compare_tokens(tokenizers, sentences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage of Tokenizer Trainer" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.utils.notebook:Google Colab not detected.\n", "INFO:ekorpkit.base:Overwriting EKORPKIT_WORKSPACE_ROOT=/workspace with /content/drive/MyDrive/workspace\n", "INFO:ekorpkit.base:Setting EKORPKIT_WORKSPACE_ROOT to /content/drive/MyDrive/workspace\n", "INFO:ekorpkit.base:Overwriting EKORPKIT_PROJECT=ekorpkit-book with ekorpkit-book\n", "INFO:ekorpkit.base:Setting EKORPKIT_PROJECT to ekorpkit-book\n", "INFO:ekorpkit.base:Overwriting EKORPKIT_PROJECT_DIR=/workspace/projects/ekorpkit-book with /content/drive/MyDrive/workspace/projects/ekorpkit-book\n", "INFO:ekorpkit.base:Loaded .env from /workspace/projects/ekorpkit-book/config/.env\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "version: 0.1.40.post0.dev37\n", "is colab? False\n", "project_dir: /content/drive/MyDrive/workspace/projects/ekorpkit-book\n", "time: 964 ms (started: 2022-11-30 06:49:42 +00:00)\n" ] } ], "source": [ "%config InlineBackend.figure_format='retina'\n", "%load_ext autotime\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "from ekorpkit import eKonf\n", "\n", "eKonf.setLogger(\"INFO\")\n", "print(\"version:\", eKonf.__version__)\n", "\n", "is_colab = eKonf.is_colab()\n", "print(\"is colab?\", is_colab)\n", "if is_colab:\n", " eKonf.mount_google_drive()\n", "workspace_dir = \"/content/drive/MyDrive/workspace\"\n", "project_name = \"ekorpkit-book\"\n", "project_dir = eKonf.set_workspace(workspace=workspace_dir, project=project_name)\n", "print(\"project_dir:\", project_dir)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:compose config with overrides: ['tokenizer=trainer']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/content/drive/MyDrive/workspace/projects/ekorpkit-book/language-modeling/data\n", "time: 506 ms (started: 2022-11-30 06:49:43 +00:00)\n" ] } ], "source": [ "data_file =\"data/enko_filtered.parquet\"\n", "\n", "cfg = eKonf.compose(\"tokenizer=trainer\")\n", "cfg.name = \"enkowiki\"\n", "cfg.batch.num_workers = 128\n", "cfg.dataset.train_file = data_file\n", "cfg._train_.use_sample = False\n", "cfg.model.model_type = \"bpe\"\n", "cfg.model.vocab_size = 30000\n", "# cfg.auto = \"train\"\n", "\n", "print(cfg.path.data_dir)\n", "# eKonf.copy(f\"{project_dir}/{data_file}\", cfg.path.data_dir)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:compose config with overrides: ['tokenizer=trainer']\n", "INFO:ekorpkit.base:Loaded .env from /workspace/projects/ekorpkit-book/config/.env\n", "INFO:ekorpkit.base:Setting WANDB_PROJECT=ekorpkit-book\n", "INFO:ekorpkit.config:Using existing path: /content/drive/MyDrive/workspace/projects/ekorpkit-book/language-modeling\n", "INFO:ekorpkit.base:No method defined to call\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time: 2.06 s (started: 2022-11-30 06:49:45 +00:00)\n" ] } ], "source": [ "from ekorpkit.tokenizers.trainer import TokenizerTrainer\n", "\n", "cfg.model.model_type = \"unigram\"\n", "trainer = TokenizerTrainer(**cfg)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['▁', 'test']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 64.5 ms (started: 2022-11-30 06:49:48 +00:00)\n" ] } ], "source": [ "trainer.tokenize(text=\"test\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:ekorpkit.config:Saving config to /content/drive/MyDrive/workspace/projects/ekorpkit-book/language-modeling/outputs/enkowiki/configs/enkowiki(11)_config.yaml\n", "INFO:ekorpkit.config:Saving config to /content/drive/MyDrive/workspace/projects/ekorpkit-book/language-modeling/outputs/enkowiki/configs/enkowiki(11)_config.json\n" ] }, { "data": { "text/plain": [ "'enkowiki(11)_config.yaml'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 267 ms (started: 2022-11-30 06:39:45 +00:00)\n" ] } ], "source": [ "trainer.save_config()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "ekorpkit print_config=false \\\n", " project_name=ekorpkit-book \\\n", " workspace_dir=/content/drive/MyDrive/workspace \\\n", " run=tokenizer \\\n", " tokenizer=trainer \\\n", " tokenizer.name=enkowiki \\\n", " tokenizer.dataset.train_file=data/enko_filtered.parquet \\\n", " tokenizer.model.model_type=unigram \\\n", " tokenizer.model.vocab_size=30000 \\\n", " tokenizer._train_.use_sample=false \\\n", " tokenizer.batch.num_workers=128 \\\n", " tokenizer.auto=train\n", "``` " ] } ], "metadata": { "colab": { "name": "corpus.ipynb", "provenance": [] }, "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": "3.8.12" }, "vscode": { "interpreter": { "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" } }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "000de79f926c4d5182c54852f73d6026": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_2ee5c88a10524257bad815cd40bdec54", "max": 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