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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/avidale/7bc6350f26196918bf339c01261f5c60/rubert-tiny.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kEMO_tYly1iA" | |
| }, | |
| "source": [ | |
| "Задача - сделать свой маленький англо-русский BERT с информативными сжатыми представлениями предложений:\n", | |
| "* Инициализируем с нуля маленький BERT (3 слоя, 45 мб весов, 12M параметров, из которых 9M - эмбеддинги) с англо-русским подмножеством словаря bert multilingual (30К токенов)\n", | |
| "* Эмбеддинги частично инициализируем из bert multilingual тоже\n", | |
| "* При обучении используем несколько лоссов:\n", | |
| " * Дистиллируем распределение выходных токенов обычного мультиязычного берта\n", | |
| " * Минимизируем whole-word MLM лосс\n", | |
| " * Минимизируем translation ranking loss, как в LaBSE\n", | |
| " * Минимизируем перплексию декодера T5, обусловленного на CLS токен и воспроизводящего текст. \n", | |
| " * Дистиллируем CLS токены, приближая разные их проекции к эмбеддингам RuBERT, LaBSE и Laser. \n", | |
| "* Обучается это всё на 2.5 млн параллельных англо-русских предложений собранных из разных корпусов. \n", | |
| "\n", | |
| "Вдохновлено:\n", | |
| "* TinyBERT от Хуавея https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D\n", | |
| "* LaBSE от Google https://arxiv.org/pdf/2007.01852.pdf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rq-7vupMKXgV" | |
| }, | |
| "source": [ | |
| "### Dependencies" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8r4axg2vWEkN" | |
| }, | |
| "source": [ | |
| "Гугл диск я подключаю, т.к. на нём находится часть обучающих данных, и туда же я регулярно сохраняю веса модели. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "eYZL1uWbzC6X", | |
| "outputId": "74f403ee-916c-47e9-f5e5-8d7cb8186d96" | |
| }, | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/gd')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Mounted at /gd\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9XQNVTpEy9ot" | |
| }, | |
| "source": [ | |
| "!pip install transformers sentencepiece datasets natasha laserembeddings" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "tJTKjScSqjwd" | |
| }, | |
| "source": [ | |
| "!pip install tensorflow_text>=2.0.0" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "BTISmp06aCoH", | |
| "outputId": "5e7dd26a-853e-4c15-ee43-5df7065f9f03" | |
| }, | |
| "source": [ | |
| "!python -m laserembeddings download-models" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Downloading models into /usr/local/lib/python3.7/dist-packages/laserembeddings/data\n", | |
| "\n", | |
| "✅ Downloaded https://dl.fbaipublicfiles.com/laser/models/93langs.fcodes \n", | |
| "✅ Downloaded https://dl.fbaipublicfiles.com/laser/models/93langs.fvocab \n", | |
| "✅ Downloaded https://dl.fbaipublicfiles.com/laser/models/bilstm.93langs.2018-12-26.pt \n", | |
| "\n", | |
| "✨ You're all set!\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9svxvMTvy39u" | |
| }, | |
| "source": [ | |
| "### Prepare" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "94VHk0fFy1iC" | |
| }, | |
| "source": [ | |
| "from transformers import BertForPreTraining, BertTokenizerFast, BertConfig" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "V9bZGdbxy1iD" | |
| }, | |
| "source": [ | |
| "base_model = 'bert-base-multilingual-cased'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "sxj7hWvry1iD" | |
| }, | |
| "source": [ | |
| "tok = BertTokenizerFast.from_pretrained(base_model)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BTRNh4UaV9QR" | |
| }, | |
| "source": [ | |
| "Корпус взят из https://translate.yandex.ru/corpus" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "R-EIHyu8y1iE" | |
| }, | |
| "source": [ | |
| "corpus_path = 'C:/Users/david/Google Диск/datasets/nlp/1mcorpus/'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Jox8iDFxy1iE" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import csv\n", | |
| "df_en = pd.read_csv(corpus_path + 'corpus.en_ru.1m.en', sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_ru = pd.read_csv(corpus_path + 'corpus.en_ru.1m.ru', sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_en.columns = ['text']\n", | |
| "df_ru.columns = ['text']\n", | |
| "\n", | |
| "print(df_ru.shape)\n", | |
| "print(df_en.shape)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "K9fvvAUiy1iF" | |
| }, | |
| "source": [ | |
| "pd.Series(len(tt) for tt in tok(df_ru.sample(10000).text.tolist())['input_ids']).describe()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "udKF0-ogy1iG" | |
| }, | |
| "source": [ | |
| "pd.Series(len(tt) for tt in tok(df_en.sample(10000).text.tolist())['input_ids']).describe()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "U9c3l_G9y1iG" | |
| }, | |
| "source": [ | |
| "### The tokenizer: initialize" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9j7tKn-Ly1iG" | |
| }, | |
| "source": [ | |
| "from collections import Counter\n", | |
| "from tqdm.auto import tqdm, trange\n", | |
| "\n", | |
| "cnt_ru = Counter()\n", | |
| "for text in tqdm(df_ru.text):\n", | |
| " cnt_ru.update(tok(text)['input_ids'])\n", | |
| " \n", | |
| "cnt_en = Counter()\n", | |
| "for text in tqdm(df_en.text):\n", | |
| " cnt_en.update(tok(text)['input_ids'])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "sp2S_n4vy1iH" | |
| }, | |
| "source": [ | |
| "print(len(cnt_ru), len(cnt_en))" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-gp_v382y1iH" | |
| }, | |
| "source": [ | |
| "print(len(sorted(k for k, v in cnt_ru.items() if v >= 5)))\n", | |
| "print(len(sorted(k for k, v in cnt_en.items() if v >= 5)))" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3hJoCpsiy1iH" | |
| }, | |
| "source": [ | |
| "print(len(sorted(k for k, v in cnt_ru.items() if v >= 10)))\n", | |
| "print(len(sorted(k for k, v in cnt_en.items() if v >= 10)))" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "39csPaYQy1iI" | |
| }, | |
| "source": [ | |
| "print(len(sorted(k for k, v in cnt_ru.items() if v >= 100)))\n", | |
| "print(len(sorted(k for k, v in cnt_en.items() if v >= 100)))" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3ZHzcaCny1iI", | |
| "outputId": "6d1e8a1a-2116-4bfb-da69-afdc871e936b" | |
| }, | |
| "source": [ | |
| "resulting_vocab = {\n", | |
| " tok.vocab[k] for k in tok.special_tokens_map.values()\n", | |
| "}\n", | |
| "for k, v in cnt_ru.items():\n", | |
| " if v >= 5 or k <= 3_000:\n", | |
| " resulting_vocab.add(k)\n", | |
| "for k, v in cnt_en.items():\n", | |
| " if v >= 100 or k <= 3_000:\n", | |
| " resulting_vocab.add(k)\n", | |
| "\n", | |
| "resulting_vocab = sorted(resulting_vocab)\n", | |
| "print(len(resulting_vocab))" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "29564\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "PtJMnr6Ry1iI" | |
| }, | |
| "source": [ | |
| "NEW_MODEL_NAME = 'tinybert-ru'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "WnLTEkjsy1iJ", | |
| "outputId": "eac88e1f-7183-4a48-e023-5144ad6deb59" | |
| }, | |
| "source": [ | |
| "tok.save_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "('tinybert-ru\\\\tokenizer_config.json',\n", | |
| " 'tinybert-ru\\\\special_tokens_map.json',\n", | |
| " 'tinybert-ru\\\\vocab.txt',\n", | |
| " 'tinybert-ru\\\\added_tokens.json')" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 33 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "V7oYbcSHy1iJ" | |
| }, | |
| "source": [ | |
| "inv_voc = {idx: word for word, idx in tok.vocab.items()}" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "yVOjjZdty1iJ" | |
| }, | |
| "source": [ | |
| "with open(NEW_MODEL_NAME + '/vocab.txt', 'w', encoding='utf-8') as f:\n", | |
| " for idx in resulting_vocab:\n", | |
| " f.write(inv_voc[idx] + '\\n')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "isSzziqJy1iJ" | |
| }, | |
| "source": [ | |
| "### The model: initialize" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "aR6BUzn3y1iK" | |
| }, | |
| "source": [ | |
| "new_tokenizer = BertTokenizerFast.from_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Ql37rRQmy1iK" | |
| }, | |
| "source": [ | |
| "small_config = BertConfig(\n", | |
| " emb_size=312,\n", | |
| " hidden_size=312,\n", | |
| " intermediate_size=600,\n", | |
| " max_position_embeddings=512,\n", | |
| " num_attention_heads=12,\n", | |
| " num_hidden_layers=3,\n", | |
| " vocab_size=new_tokenizer.vocab_size,\n", | |
| ")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kYudfvpfy1iK" | |
| }, | |
| "source": [ | |
| "small_model = BertForPreTraining(small_config)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "8T1dsHTny1iK" | |
| }, | |
| "source": [ | |
| "small_model.save_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BU06VFHMy1iK" | |
| }, | |
| "source": [ | |
| "Выкачиваем веса из большой модели для инициализации" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "eDfDgaTny1iL", | |
| "outputId": "f5f9126d-b159-4653-c731-a79b0ef7fa03" | |
| }, | |
| "source": [ | |
| "big_model = BertForPreTraining.from_pretrained(base_model)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Some weights of BertForPreTraining were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['cls.predictions.decoder.bias']\n", | |
| "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "4fehLoEiy1iL" | |
| }, | |
| "source": [ | |
| "# copy input embeddings\n", | |
| "small_model.bert.embeddings.word_embeddings.weight.data = big_model.bert.embeddings.word_embeddings.weight.data[resulting_vocab, :312].clone()\n", | |
| "small_model.bert.embeddings.position_embeddings.weight.data = big_model.bert.embeddings.position_embeddings.weight.data[:, :312].clone()\n", | |
| "# copy output embeddings\n", | |
| "small_model.cls.predictions.decoder.weight.data = big_model.cls.predictions.decoder.weight.data[resulting_vocab, :312].clone()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "097Jhjzry1iL" | |
| }, | |
| "source": [ | |
| "small_model.save_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QhhbVcCay1iL" | |
| }, | |
| "source": [ | |
| "### Fine tune the model (multitask and distillation)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "GryEO7tjzXaA" | |
| }, | |
| "source": [ | |
| "NEW_MODEL_NAME = 'tinybert-ru'\n", | |
| "NEW_MODEL_NAME = '/gd/MyDrive/models/tinybert-ru'\n", | |
| "base_model = 'bert-base-multilingual-cased'\n", | |
| "#corpus_path = 'C:/Users/david/Google Диск/datasets/nlp/1mcorpus/'\n", | |
| "corpus_path = '/gd/MyDrive/datasets/nlp/1mcorpus/'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vxKUbgdIy1iL" | |
| }, | |
| "source": [ | |
| "from transformers import BertForPreTraining, BertTokenizerFast, BertConfig" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "v20PtZrIy1iM" | |
| }, | |
| "source": [ | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import torch.nn.functional as F" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NndT9QKdy1iM" | |
| }, | |
| "source": [ | |
| "from tqdm.auto import tqdm, trange" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "nonXL8bFy1iM" | |
| }, | |
| "source": [ | |
| "#### Prepare data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "7W4TGrrDy1iM", | |
| "outputId": "b8026624-1919-4492-c1f8-23981d8f8cfc" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import csv\n", | |
| "df_en = pd.read_csv(corpus_path + 'corpus.en_ru.1m.en', sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_ru = pd.read_csv(corpus_path + 'corpus.en_ru.1m.ru', sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", | |
| "df_en.columns = ['text']\n", | |
| "df_ru.columns = ['text']\n", | |
| "\n", | |
| "print(df_ru.shape)\n", | |
| "print(df_en.shape)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "(1000000, 1)\n", | |
| "(1000000, 1)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0A3J_45PvlGZ" | |
| }, | |
| "source": [ | |
| "Добавляем датасеты opus100 и tatoeba, чтобы разнообразить примеры\n", | |
| "\n", | |
| "https://huggingface.co/datasets/opus100\n", | |
| "\n", | |
| "https://huggingface.co/datasets/tatoeba" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 289, | |
| "referenced_widgets": [ | |
| "0a52fe88c96b4cd6ad3419b76500a7f5", | |
| "7efe537454eb4797b8b78b5446d1aeeb", | |
| "b25796d6816a419493e51707fa382837", | |
| "1cc992a5ad7d49408faa54108df0fb7c", | |
| "800be8a4516c41f9a2221222d0f175fa", | |
| "9053db753786459cbce371472b1b5647", | |
| "c422afc902854ae5a1a62e7d86bc5ba8", | |
| "a5c64dc937184d36af38b5112b2bce82", | |
| "4439025ae3aa449e98a6b93aca49d6cd", | |
| "015645e2bc434295a6fd8752ca985bea", | |
| "43f0dd9742ab4962bd67d776a3fbfb4b", | |
| "df9ebae2913948479b8e34c9238c47a8", | |
| "a5cc62ccf350423fab12ab98f222d70e", | |
| "abd6844fc9b64f569ee1d9fe3759feae", | |
| "ce70ee84d0954e1e861d0072d8b2815d", | |
| "9433190ace91411e9e15e0c2ee43084d", | |
| "31352c7a09c6416581d37d0ca9249cc7", | |
| "c7c64bb990f2462792ef9e3fe5444d27", | |
| "463248de93c641ff836da752e2e96f36", | |
| "7672f5a6ddac4154b9ee9dc107bda350", | |
| "418ad1bf1e2b4c6dba5b52748accf3d5", | |
| "c6ccbcc2bd18435fa0677d9b0ac4218f", | |
| "c59b80c09ab345ebb28e4a75fcf2031c", | |
| "6cc9689b0cb04ce9af56c9bdcd575292", | |
| "dabf86c8ea8d422a8609ae2712e7bae3", | |
| "2fd17344fc2c4f86b14195521902519f", | |
| "4f8858f4b10b469a8384b979b2ba72a2", | |
| "5fcd8ad7ddf647dba17c6078af40d915", | |
| "4d6c62fcd63648f681a7add2af68dac4", | |
| "16b9ad470df9491ba89dbf7235a28b06", | |
| "a75a0c9bd112445a9298486196ea0274", | |
| "28d68f14a6704f19ac164391e0454a4b", | |
| "69ccb24556874ea2a888474745f84264", | |
| "5a4c7eb5bc0242cc897ff0205d049abe", | |
| "3c87620a0b4b43d4a839c7cb57b0ed08", | |
| "6cd9c354332c48708d2bd972165df0be", | |
| "0307ce401df648eb8a53f9e28b53591d", | |
| "d44027a88ccb4bcb90ec41c4e0db0167", | |
| "0a399b8c74a746578ea80efd1da5104d", | |
| "954094a06e274d758c3931a2743b43cc" | |
| ] | |
| }, | |
| "id": "ZgQHrZ2aqhU8", | |
| "outputId": "16f2edfa-c4d3-4dab-99cc-240fa5e3e3aa" | |
| }, | |
| "source": [ | |
| "from datasets import load_dataset\n", | |
| "tatoeba = load_dataset(\"tatoeba\", lang1=\"en\", lang2=\"ru\")\n", | |
| "\n", | |
| "tat_ru = []\n", | |
| "tat_en = []\n", | |
| "for pair in tqdm(tatoeba['train']):\n", | |
| " tat_ru.append(pair['translation']['ru'])\n", | |
| " tat_en.append(pair['translation']['en'])\n", | |
| "\n", | |
| "df_en = pd.concat([df_en, pd.Series(tat_en, name='text').to_frame()])\n", | |
| "df_ru = pd.concat([df_ru, pd.Series(tat_ru, name='text').to_frame()])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "0a52fe88c96b4cd6ad3419b76500a7f5", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1896.0, style=ProgressStyle(description…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "4439025ae3aa449e98a6b93aca49d6cd", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1438.0, style=ProgressStyle(description…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Using custom data configuration en-ru-lang1=en,lang2=ru\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Downloading and preparing dataset tatoeba/en-ru (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/tatoeba/en-ru-lang1=en,lang2=ru/0.0.0/54423b66d13968ea583b6ac5828448a54b1a69944cabd3368ccd364fdb4f3216...\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "31352c7a09c6416581d37d0ca9249cc7", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=14047102.0, style=ProgressStyle(descrip…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "dabf86c8ea8d422a8609ae2712e7bae3", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\rDataset tatoeba downloaded and prepared to /root/.cache/huggingface/datasets/tatoeba/en-ru-lang1=en,lang2=ru/0.0.0/54423b66d13968ea583b6ac5828448a54b1a69944cabd3368ccd364fdb4f3216. Subsequent calls will reuse this data.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "69ccb24556874ea2a888474745f84264", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, max=514195.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 272, | |
| "referenced_widgets": [ | |
| "15eb862a14ab4a8290659c5be4165512", | |
| "48253d09475a48f286288d389b129a49", | |
| "6fc002d18c8645e8a87a0d02df05820f", | |
| "8105accd26264b2ca4811696bab672c0", | |
| "a5a2a96e74ba44ad926af4298cb493ea", | |
| "f4f688aa239341a6a53a9c53e8801910", | |
| "0926dce034e64aebb525043f8d805eb4", | |
| "99c87db5323246d9b3a3e3f31401d686", | |
| "e9a9e0950ddf48df980a7d64cdf0d113", | |
| "35fc1bf189e841d49f45dee21432a6fd", | |
| "b3baf8cc045e4d72bd9360df4766273f", | |
| "eb53eb69ea3d43e195444ba540d84fd7", | |
| "b42f89384ce145b4abfd926a2bf5b76f", | |
| "d686a207b3dc4ad79b026bd7f75a840b", | |
| "aab7cd25a05843ed92ad8669e7d4c598", | |
| "133cd5e05c794709a4c83d244cd14474", | |
| "560831b29a0d4de29fe785cc4ece0610", | |
| "07a0a865c2ab417d9a29ec7a671c4151", | |
| "e30e1ba86c5e418cb22c06c6a6059341", | |
| "4eb26191b7a744598581356ec150127f", | |
| "98b2e77e61de45c681f7e608ac7e4109", | |
| "61865f221cfe404b9b39a69090644507", | |
| "153bbb6533184782a4a03507d70b81e6", | |
| "2a388c75761c48ddb43c3a30cbc6e6ad", | |
| "a9fff4a599da446a9b4b52e8c696b81e", | |
| "7209b0d38d8341d681a96eb542aebb8f", | |
| "81f9204291df4e49b80fd21d4e057c70", | |
| "c909d711a42943a8a2152fc09f25be98", | |
| "d0a1fb9dbb6c4ef7944db9b82ede6e5d", | |
| "7625b1d1090641b0842d0553cb0e7e66", | |
| "10129c224cef4f82b893453b0e5578dd", | |
| "cb74b9c81d0746fd8a44973d5d2544f7", | |
| "20eb0b7ab6584d84a7f7ab7477040e3d", | |
| "9785e07c64ca4068ad99a65da326b1aa", | |
| "588ff0d4b83f439cb8787314267ad6ea", | |
| "b6c532398f4045d184600d1bf7849b54", | |
| "f7258ebdafef444281c048fbece5adab", | |
| "50448247069f4330b2d4114abd2523fc", | |
| "2651f7dced1d4d7b9c396afb8b7dd7be", | |
| "f5e05f9dbb3b4fe0b4ecd0041fab282f", | |
| "234662f3a8d248d19cc52da034a842f0", | |
| "cc136fac62654de3a029d25cb7644ad4", | |
| "cf1a6777af99454dbf08cf86ab342132", | |
| "54a7d7995a1d4073998d7f43f4ea877c", | |
| "c11ff305c42d42d781ea618658f622db", | |
| "d14040f5a53c4410a58d2a871612248a", | |
| "026efcab8c1b48739e1b521a5df70316", | |
| "373a2e36f38c4244aa80bc90405bbec0", | |
| "79268e2e1c46422e889831b91236bc88", | |
| "f0fea403e7054f2594643f4f2e1f32df", | |
| "54108263ed2241809b0669a3c786d4bb", | |
| "ddbb28e74716429fb30f9134744770db", | |
| "78e8b052d7024a73afa04cb0dc4b67e2", | |
| "9de36f7dd9d442e197cc12be859b7772", | |
| "028906a5a0b648a9a99a698e14c740e1", | |
| "6275954e5e824a5db4868b24e7876096" | |
| ] | |
| }, | |
| "id": "j46XD99mrNaW", | |
| "outputId": "1c49e11a-5210-47e9-aae5-ac834268560f" | |
| }, | |
| "source": [ | |
| "from datasets import load_dataset\n", | |
| "opus100 = load_dataset('opus100', 'en-ru')\n", | |
| "\n", | |
| "tat_ru = []\n", | |
| "tat_en = []\n", | |
| "for pair in tqdm(opus100['train']):\n", | |
| " tat_ru.append(pair['translation']['ru'])\n", | |
| " tat_en.append(pair['translation']['en'])\n", | |
| "\n", | |
| "df_en = pd.concat([df_en, pd.Series(tat_en, name='text').to_frame()])\n", | |
| "df_ru = pd.concat([df_ru, pd.Series(tat_ru, name='text').to_frame()])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "15eb862a14ab4a8290659c5be4165512", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2216.0, style=ProgressStyle(description…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "e9a9e0950ddf48df980a7d64cdf0d113", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=13292.0, style=ProgressStyle(descriptio…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Downloading and preparing dataset opus100/en-ru (download: 65.33 MiB, generated: 187.00 MiB, post-processed: Unknown size, total: 252.33 MiB) to /root/.cache/huggingface/datasets/opus100/en-ru/0.0.0/a87abd612d82947c7a2c3991f71095a98f55141af7ad37516dfb31bfa3511ddc...\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "560831b29a0d4de29fe785cc4ece0610", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=68501634.0, style=ProgressStyle(descrip…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "a9fff4a599da446a9b4b52e8c696b81e", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\r" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "20eb0b7ab6584d84a7f7ab7477040e3d", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\r" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "234662f3a8d248d19cc52da034a842f0", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\rDataset opus100 downloaded and prepared to /root/.cache/huggingface/datasets/opus100/en-ru/0.0.0/a87abd612d82947c7a2c3991f71095a98f55141af7ad37516dfb31bfa3511ddc. Subsequent calls will reuse this data.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "79268e2e1c46422e889831b91236bc88", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, max=1000000.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vhqqxLEp7JZU" | |
| }, | |
| "source": [ | |
| "df_ru.reset_index(drop=True, inplace=True);\n", | |
| "df_en.reset_index(drop=True, inplace=True);" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3n9BOCdnrO8i", | |
| "outputId": "06169bc8-ba7b-4d49-f29e-0bc81ec558da" | |
| }, | |
| "source": [ | |
| "df_en.shape, df_ru.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "((2514195, 1), (2514195, 1))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cN5zsEue6Ny7" | |
| }, | |
| "source": [ | |
| "Сложные негативные примеры (в итоге забил на них)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "i_unmC9g6Bez" | |
| }, | |
| "source": [ | |
| "import math\n", | |
| "\n", | |
| "from collections import Counter, defaultdict\n", | |
| "from functools import lru_cache\n", | |
| "from typing import List, Dict\n", | |
| "from tqdm.auto import tqdm, trange\n", | |
| "import re\n", | |
| "\n", | |
| "TOKEN = re.compile(r'([^\\W\\d]+|\\d+|[^\\w\\s])')\n", | |
| "\n", | |
| "\n", | |
| "def re_tokenize(text):\n", | |
| " chunks = TOKEN.findall(text)\n", | |
| " return find_substrings(chunks, text)\n", | |
| "\n", | |
| "\n", | |
| "def find_substrings(chunks, text):\n", | |
| " offset = 0\n", | |
| " for chunk in chunks:\n", | |
| " start = text.find(chunk, offset)\n", | |
| " stop = start + len(chunk)\n", | |
| " yield chunk\n", | |
| " offset = stop\n", | |
| "\n", | |
| "\n", | |
| "class SimpleSearcher:\n", | |
| " def __init__(self, k=1.5, b=0.75, max_freq=None, df=False):\n", | |
| " self.k = k\n", | |
| " self.b = b\n", | |
| " self.max_freq = max_freq\n", | |
| " self.df = df\n", | |
| "\n", | |
| " def tokenize(self, text, stem=None):\n", | |
| " return list(re_tokenize(text.lower()))\n", | |
| "\n", | |
| " def setup(self, texts, owners):\n", | |
| " \"\"\" texts: list of texts, owners: list of ids \"\"\"\n", | |
| " self.texts = texts\n", | |
| " self.owners = owners\n", | |
| " paragraphs = {i: text for i, text in enumerate(texts)}\n", | |
| " self.fit(paragraphs=paragraphs)\n", | |
| " return self\n", | |
| "\n", | |
| " def fit(self, paragraphs):\n", | |
| " \"\"\"\" paragraphs: dict with ids as keys and texts as values \"\"\"\n", | |
| " inverse_index = defaultdict(set)\n", | |
| " text_frequencies = Counter()\n", | |
| " text_lengths = Counter()\n", | |
| " wf = Counter()\n", | |
| " for p_id, p in tqdm(paragraphs.items(), total=len(paragraphs)):\n", | |
| " tokens = self.tokenize(p)\n", | |
| " text_lengths[p_id] = len(tokens)\n", | |
| " for w in tokens:\n", | |
| " wf[w] += 1\n", | |
| " if self.max_freq and wf[w] >= self.max_freq:\n", | |
| " inverse_index[w] = set()\n", | |
| " else:\n", | |
| " inverse_index[w].add(p_id)\n", | |
| " \n", | |
| " self.inverse_index = inverse_index\n", | |
| " self.wf = wf\n", | |
| " self.text_lengths = text_lengths\n", | |
| " self.avg_len = sum(text_lengths.values()) / len(text_lengths)\n", | |
| " self.n_docs = len(paragraphs)\n", | |
| " \n", | |
| " def trim(self, n):\n", | |
| " # remove \"stopwords\" - words with too many indices\n", | |
| " stopwords = {k for k, v in self.inverse_index.items() if len(v) > n}\n", | |
| " for k in stopwords:\n", | |
| " self.inverse_index[k] = set()\n", | |
| "\n", | |
| " def get_okapi_idf(self, w):\n", | |
| " n = self.wf[w]\n", | |
| " return math.log(max(1, self.n_docs - n + 0.5) / (n + 0.5))\n", | |
| "\n", | |
| " def get_okapi_tf(self, w, p_id):\n", | |
| " f = self.text_frequencies[(p_id, w)] if self.df else 1\n", | |
| " return f * (self.k + 1) / (f + self.k * (1 - self.b + self.b * self.text_lengths[p_id] / self.avg_len))\n", | |
| "\n", | |
| " def get_tf_idfs(self, query):\n", | |
| " words = self.tokenize(query)\n", | |
| " matches = [(w, d) for w in words for d in self.inverse_index[w]]\n", | |
| "\n", | |
| " tfidfs = Counter()\n", | |
| " for w, d in matches:\n", | |
| " tfidfs[d] += self.text_frequencies[(d, w)] / len(self.inverse_index[w])\n", | |
| "\n", | |
| " return tfidfs\n", | |
| "\n", | |
| " def get_okapis(self, query, normalize=False):\n", | |
| " words = self.tokenize(query)\n", | |
| " matches = [(w, d) for w in words for d in self.inverse_index[w]]\n", | |
| "\n", | |
| " tfidfs = Counter()\n", | |
| " for w, d in matches:\n", | |
| " tfidfs[d] += self.get_okapi_idf(w) * self.get_okapi_tf(w, d)\n", | |
| "\n", | |
| " return tfidfs" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 67, | |
| "referenced_widgets": [ | |
| "1e81ca98918b442e889e196645e1ca47", | |
| "378bd07de8ea49da995a9eeef4adb132", | |
| "b9097818a3e84b3fb224bdb5162be393", | |
| "b88ee8c94000445db99d299aecca40ca", | |
| "6e7166d1f6f34c81a44595750f3022ca", | |
| "eb9c7b60c0f1484b80a020efec9e31b3", | |
| "ef1ce741d6b44be6b21113f3d8d21629", | |
| "8596aad5509247a2971fd22acaae7b3f" | |
| ] | |
| }, | |
| "id": "4ScwUFPt6CcP", | |
| "outputId": "0df3285e-b7a9-4d14-8fdd-d6f259fd5c33" | |
| }, | |
| "source": [ | |
| "ss = SimpleSearcher(max_freq=10_000)\n", | |
| "ss.fit(df_ru.text.sample(100).to_dict())\n", | |
| "#ss.fit(df_ru.text.to_dict())" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "1e81ca98918b442e889e196645e1ca47", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-Ig_sTnr65lW" | |
| }, | |
| "source": [ | |
| "def hard_batch(n=16):\n", | |
| " text = df_ru.text.sample(1).iloc[0]\n", | |
| " indices = [k for k, v in ss.get_okapis(text).most_common(n * 4)]\n", | |
| " indices = df_ru.text[indices].drop_duplicates().index.tolist()[:n]\n", | |
| " if len(indices) < n:\n", | |
| " indices.extend(df_ru.text.sample(n - len(indices)).index)\n", | |
| " return indices" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gjhJwHFQDca_", | |
| "outputId": "950f8c3c-c0b2-4059-82b1-685f92acb0bd" | |
| }, | |
| "source": [ | |
| "%%time\n", | |
| "for _ in df_ru.text[hard_batch(16)]:\n", | |
| " print(_)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Том сел на камень.\n", | |
| "Ответ на повторяющиеся мероприятия\n", | |
| "– Написать письмо претендуете на должность;\n", | |
| "Том в костюме.\n", | |
| "Том был голый.\n", | |
| "Том делает это гораздо лучше, чем я.\n", | |
| "ссылаясь также на свою резолюцию 56/260 от 31 января 2002 года, в которой Ассамблея установила мандат Специального комитета по разработке конвенции против коррупции,\n", | |
| "Кроме того, Комитет рекомендует государству-участнику в свете статьи 17 Конвенции принимать все необходимые меры законодательного и иного характера, включая, в частности, проведение просветительских кампаний, направленных на родителей, попечителей и учителей, а также налаживать сотрудничество с провайдерами услуг Интернета в целях защиты детей от пагубного воздействия передаваемой через средства массовой коммуникации и через Интернет информации, в том числе материалы, содержащие сцены насилия и порнографии.\n", | |
| "Мы твердо уверены в том, что необходимо безотлагательно создать влиятельную группу видных деятелей, которая займется этим вопросом.\n", | |
| "В связи с окончательной формой документа ряд членов Комиссии согласились со Специальным докладчиком в том, что решение по этому вопросу следует отложить, пока не будут согласованы вопросы существа.\n", | |
| "В составе ракетного сегмента этого комплекса - РН \"Зенит-2S\", которая является модификацией \"Зенита\", имевшего единую модульную часть с боковыми блоками первой ступени РН \"Энергия\", и космический разгонный блок ДМ-SL как одна из 10 модификаций высоконадежного, эксплуатируемого в течение нескольких десятилетий по различным программам разгонного блока типа ДМ, работающего на компонентах топлива жидкий кислород и керосин.\n", | |
| "4. вновь заявляет также, что в отношении областей, в которых консультанты часто нанимаются на срок, превышающий один год, Генеральному секретарю следует, когда это необходимо, представлять предложения о создании должностей и что ему следует представить Генеральной Ассамблее на ее шестьдесят первой сессии доклад по этому вопросу;\n", | |
| "Сезонная продукция: Расчет спроса на будущий месяц, основанный на среднем уровне потребления, зафиксированного в шести месяцах, предшествующих тому же месяцу что и текущий в прошлом году, и на последующем применении тренда, отражающего изменение объема продаж.\n", | |
| "В прессе звучали слова об ответственности Грызлова за проникновение отряда террористов в столицу, но никакой реакции на эти публикации не последовало - ГосДума отказалась создавать комиссию для расследования обстоятельств теракта и гибели людей.\n", | |
| "Слова из Деяний 24:15 проливают еще больше света на это грандиозное событие: \"Будет воскресение и праведных, и неправедных\".\n", | |
| "Эта учетная запись должна иметь учетные данные локального администратора для подключения к указанному компьютеру, на котором будут установлены роли систем сайта.\n", | |
| "CPU times: user 89.8 ms, sys: 181 µs, total: 90 ms\n", | |
| "Wall time: 91.5 ms\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "38_tq7ghKJdr", | |
| "outputId": "9e2637a1-5413-4d7b-f1b6-ceebe81854f2" | |
| }, | |
| "source": [ | |
| "import gc\n", | |
| "gc.collect()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "73" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "JDGAMwEY59g8" | |
| }, | |
| "source": [ | |
| "#### Setup the model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZqnNjd2CuJ3T" | |
| }, | |
| "source": [ | |
| "model = BertForPreTraining.from_pretrained(NEW_MODEL_NAME)\n", | |
| "tokenizer = BertTokenizerFast.from_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_k-ynLBRuPZl" | |
| }, | |
| "source": [ | |
| "import gc\n", | |
| "\n", | |
| "def cleanup():\n", | |
| " gc.collect()\n", | |
| " torch.cuda.empty_cache()\n", | |
| " # tf.keras.backend.clear_session()\n", | |
| "\n", | |
| "cleanup()\n", | |
| "\n", | |
| "model.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pq3R5RM7y1iO" | |
| }, | |
| "source": [ | |
| "from transformers import DataCollatorForWholeWordMask\n", | |
| "\n", | |
| "data_collator = DataCollatorForWholeWordMask(\n", | |
| " tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n", | |
| ")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UINY4LZfy1iO" | |
| }, | |
| "source": [ | |
| "from tqdm.auto import tqdm, trange" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "KW4D5rHaulPB" | |
| }, | |
| "source": [ | |
| "def get_sentence_loss(out_ru, out_en, margin=0.3, mult=1.0):\n", | |
| " \"\"\" Calculate translation ranking loss using CLS tokens \"\"\"\n", | |
| " emb_ru = F.normalize(out_ru) #(out_ru.hidden_states[-1][:, 0])\n", | |
| " emb_en = F.normalize(out_en) #(out_en.hidden_states[-1][:, 0])\n", | |
| " batch_size = emb_ru.shape[0]\n", | |
| " sims = torch.matmul(emb_ru, emb_en.T) - torch.eye(batch_size).cuda() * margin\n", | |
| " loss_fn = torch.nn.CrossEntropyLoss()\n", | |
| " loss = (\n", | |
| " loss_fn(torch.log_softmax(sims, -1) * mult, torch.arange(batch_size).cuda())\n", | |
| " + loss_fn(torch.log_softmax(sims.T, -1) * mult, torch.arange(batch_size).cuda())\n", | |
| " )\n", | |
| " return loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ckum58IPrY_n" | |
| }, | |
| "source": [ | |
| "```\n", | |
| "# demontrate that the task is difficult\n", | |
| "emb_ru = F.normalize(pool_ru)\n", | |
| "emb_en = F.normalize(pool_en)\n", | |
| "batch_size = emb_ru.shape[0]\n", | |
| "sims = torch.matmul(emb_ru, emb_en.T) - torch.eye(batch_size).cuda() * margin \n", | |
| "print(torch.softmax(sims, dim=1).diag().mean()) # about 0.09\n", | |
| "\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "plt.imshow(torch.softmax(sims, dim=1).detach().cpu().numpy())\n", | |
| "\n", | |
| "emb_ru, emb_en, sims = None, None, None\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BUQvV3uDy1iP" | |
| }, | |
| "source": [ | |
| "def get_mask_labels(input_ids):\n", | |
| " mask_labels = []\n", | |
| " for e in input_ids:\n", | |
| " ref_tokens = []\n", | |
| " for idx in e:\n", | |
| " token = tokenizer._convert_id_to_token(idx)\n", | |
| " ref_tokens.append(token)\n", | |
| " mask_labels.append(data_collator._whole_word_mask(ref_tokens))\n", | |
| " ml = torch.tensor(mask_labels)\n", | |
| " inputs, labels = data_collator.mask_tokens(input_ids, ml)\n", | |
| " return inputs, labels" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AalTMbZqy1iP" | |
| }, | |
| "source": [ | |
| "def preprocess_inputs(inputs):\n", | |
| " inputs['input_ids'], inputs['labels'] = get_mask_labels(inputs['input_ids'])\n", | |
| " return {k: v.to(model.device) for k, v in inputs.items()}" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "iAGk9slgy1iP" | |
| }, | |
| "source": [ | |
| "def get_mlm_loss(inputs, outputs):\n", | |
| " return nn.CrossEntropyLoss()(\n", | |
| " outputs.prediction_logits.view(-1, model.config.vocab_size),\n", | |
| " inputs['labels'].view(-1)\n", | |
| " )" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "43AeFcYVBlQa" | |
| }, | |
| "source": [ | |
| "def pool(model, outputs):\n", | |
| " return model.bert.pooler(outputs.hidden_states[-1])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "v0vqSGD8uYya" | |
| }, | |
| "source": [ | |
| "#### MLM distillation loss" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "referenced_widgets": [ | |
| "5a299c646f73422b94f086ed3fc40d07", | |
| "a90da2d4f68749eba5441d4fc305fa36", | |
| "cdcabf402d6d4f08ac3d65257a435734", | |
| "5ad80080b71441d9bfe16c41a838750a", | |
| "59f5cfc0e8934ca29705583f702f6aa6", | |
| "6fa9ce85f1484bf6a49f4a019700d617", | |
| "54582b5aafd649bbb6c30d9a2635a132", | |
| "a261543b77454ee4ba4ea2b36b543332", | |
| "8b4ff98bc1e54aaf8125bdb3d8714927", | |
| "f5d89146e7fa48529b9be8f00eeee9b9", | |
| "46eca66cf3544a0490ac156cb213c353", | |
| "8dea7e2e0e1c490ba8c5c98eaa804001", | |
| "eeaba765569f4484a9e3b98d9142360a", | |
| "9d4314d4febf4bb08d3438e6d69418be", | |
| "00c53b121ffc4c2bb4b7086bda5c303d", | |
| "66f0822b93554f16b30050ced4f7d85c", | |
| "d867eb2270344ab08b4b7bed3a515a98", | |
| "c6d5340668a940b98ebab206cec8bfe5", | |
| "a4df8e2c70eb4ff1a8172b7b74e7590e", | |
| "b29ef88147be4684ae7cd9c728d2bb11", | |
| "008f4b212204435abc4cb6ac835526cd", | |
| "2d7286b1ffa545dbb70734d04b50bf49", | |
| "44f57c89d335475396501f0178a6dc9b", | |
| "4d3f057bc56b481c9583f77241a2c3e6", | |
| "b12451cce3dc43b29589659227545677", | |
| "392da82bed954990a4710398dfcf43ee", | |
| "17a8beb0ecb5436288065f07dfc43347", | |
| "7203f603977d4f63a1b5e04678529bf9", | |
| "35f7f7c515cd438d86709439f8f6cbea", | |
| "f6b9b945f72941389668855243292334", | |
| "ab28c4129ec54b33a598b906f9a7476c", | |
| "c80fa2fd45ce4f9c8bd18cc37125ae57", | |
| "3eb695ed9df542a780c78898061f1b72", | |
| "1758e6f7d50d41c3b7e7ccc81d415a15", | |
| "13bdeb14a3c640039aab5195476c3a38", | |
| "3ae0ae9c625b41649b9c03af595dc067", | |
| "c7938c61126d4dbcba28ef9e5c201628", | |
| "58f64df9ec274bb1bab78464f1fce3d1", | |
| "e1a52c00073f4114b9b3ae3c2938c9f0", | |
| "1394f29831cc461db28b37e1b9fbceb1" | |
| ] | |
| }, | |
| "id": "CDUIL42ludym", | |
| "outputId": "ed425191-bd96-4cac-f8ca-4254e2759e06" | |
| }, | |
| "source": [ | |
| "big_model = BertForPreTraining.from_pretrained(base_model)\n", | |
| "big_tokenizer = BertTokenizerFast.from_pretrained(base_model)\n", | |
| "\n", | |
| "bv = big_tokenizer.vocab\n", | |
| "vocab_mapping = sorted(bv[w] for w in tokenizer.vocab)\n", | |
| "\n", | |
| "big_model.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "5a299c646f73422b94f086ed3fc40d07", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=625.0, style=ProgressStyle(description_…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "8b4ff98bc1e54aaf8125bdb3d8714927", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=714314041.0, style=ProgressStyle(descri…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Some weights of BertForPreTraining were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['cls.predictions.decoder.bias']\n", | |
| "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "d867eb2270344ab08b4b7bed3a515a98", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=995526.0, style=ProgressStyle(descripti…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "b12451cce3dc43b29589659227545677", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1961828.0, style=ProgressStyle(descript…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "3eb695ed9df542a780c78898061f1b72", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=29.0, style=ProgressStyle(description_w…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AUzvKrWKuiZS" | |
| }, | |
| "source": [ | |
| "def distill(inputs, outputs, temperature=1.0):\n", | |
| " new_inputs = torch.tensor(\n", | |
| " [[vocab_mapping[i] for i in row] for row in inputs['input_ids']]\n", | |
| " ).to(inputs['input_ids'].device)\n", | |
| " with torch.no_grad():\n", | |
| " big_out = big_model(\n", | |
| " input_ids=new_inputs, \n", | |
| " token_type_ids=inputs['token_type_ids'],\n", | |
| " attention_mask=inputs['attention_mask']\n", | |
| " )\n", | |
| " # the whole batch, all tokens after the [cls], the whole dimension\n", | |
| " kd_loss = torch.nn.KLDivLoss(reduction='batchmean')(\n", | |
| " F.log_softmax(outputs.prediction_logits[:, 1:, :] / temperature, dim=1), \n", | |
| " F.softmax(big_out.prediction_logits[:, 1:, vocab_mapping] / temperature, dim=1)\n", | |
| " ) / outputs.prediction_logits.shape[-1]\n", | |
| " return kd_loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "44WxqlsjOIgY" | |
| }, | |
| "source": [ | |
| "#### Sentence distillation loss (LaBSE, RuBERT, Laser, USE)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zrW-A4KsOdsD" | |
| }, | |
| "source": [ | |
| "from transformers import AutoModel, AutoTokenizer" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "a7mljjW6GBzA" | |
| }, | |
| "source": [ | |
| "# тут я пытался сделать адаптеры нелинейными, но забил\n", | |
| "\n", | |
| "class Adapter(nn.Module):\n", | |
| " def __init__(self, n_in, n_out):\n", | |
| " super().__init__()\n", | |
| " self.proj0 = nn.Linear(n_in, n_in)\n", | |
| " self.proj1 = nn.Linear(n_in, n_in)\n", | |
| " self.proj2 = nn.Linear(n_in, n_in)\n", | |
| " self.proj3 = nn.Linear(n_in, n_out)\n", | |
| " self.nonlin = nn.CELU()\n", | |
| " \n", | |
| " def forward(self, x):\n", | |
| " x = nn.functional.normalize(self.proj0(x))\n", | |
| " x = x + self.nonlin(self.proj1(x))\n", | |
| " x = x + self.nonlin(self.proj2(x))\n", | |
| " x = nn.functional.normalize(self.proj3(x))\n", | |
| " return x\n", | |
| "\n", | |
| "# undo\n", | |
| "Adapter = torch.nn.Linear" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "26VzhwwyONR4", | |
| "outputId": "33da1fcc-a1bb-4a3e-992f-5783dafdabba" | |
| }, | |
| "source": [ | |
| "labse_name = '/gd/MyDrive/models/labse-ru'\n", | |
| "labse = AutoModel.from_pretrained(labse_name)\n", | |
| "labse_tokenizer = AutoTokenizer.from_pretrained(labse_name)\n", | |
| "\n", | |
| "labse.eval()\n", | |
| "labse.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Some weights of the model checkpoint at /gd/MyDrive/models/labse-ru were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias']\n", | |
| "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |
| "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0t-Nl-toPGBD", | |
| "outputId": "fa17704d-f489-402b-fec1-17f0194a929f" | |
| }, | |
| "source": [ | |
| "import os\n", | |
| "ladapter_path = '/gd/MyDrive/models/tinybert-ru-labse-adapter.pt'\n", | |
| "if os.path.exists(ladapter_path):\n", | |
| " labse_adapter = torch.load(ladapter_path)\n", | |
| " print('loading')\n", | |
| "else:\n", | |
| " labse_adapter = Adapter(312, 768)\n", | |
| " print('creating from scratch')\n", | |
| "labse_adapter.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "loading\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZxcxjO1dOiCi" | |
| }, | |
| "source": [ | |
| "def get_labse_loss(outputs_list, texts):\n", | |
| " inp = {k: v.to(labse.device) for k, v in labse_tokenizer(texts, return_tensors='pt', padding=True, max_length=512, truncation=True).items()}\n", | |
| " with torch.no_grad():\n", | |
| " labse_out = labse(**inp)\n", | |
| " emb = torch.nn.functional.normalize(labse_out.pooler_output)\n", | |
| " lfun = torch.nn.MSELoss()\n", | |
| " loss = sum([\n", | |
| " lfun(torch.nn.functional.normalize(labse_adapter(out)), emb) \n", | |
| " for out in outputs_list\n", | |
| " ])\n", | |
| " return loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ASyRfW9cGB7a" | |
| }, | |
| "source": [ | |
| "The clever plan is that we will pull our embeddings to LaBSE embeddings of the English sentences and to rubert embeddings of the Russian sentences at the same time. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 284, | |
| "referenced_widgets": [ | |
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| "4f6aabfc6ac44937a2630e0ad150d831", | |
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| "7c4cb05f930f40fbb96f13552a5bc60c", | |
| "36b7c06256b94a9ab6679eb7fe3e6ff3" | |
| ] | |
| }, | |
| "id": "Fw728FA8C5Rp", | |
| "outputId": "1d6e14ad-953a-47ef-af5b-2c1f18277e15" | |
| }, | |
| "source": [ | |
| "rubert_name = 'DeepPavlov/rubert-base-cased-sentence'\n", | |
| "rubert = AutoModel.from_pretrained(rubert_name)\n", | |
| "rubert_tokenizer = AutoTokenizer.from_pretrained(rubert_name)\n", | |
| "\n", | |
| "rubert.eval()\n", | |
| "rubert.cuda();\n", | |
| "\n", | |
| "\n", | |
| "import os\n", | |
| "dpadapter_path = '/gd/MyDrive/models/tinybert-ru-rubert-adapter.pt'\n", | |
| "if os.path.exists(dpadapter_path):\n", | |
| " dp_adapter = torch.load(dpadapter_path)\n", | |
| " print('loading')\n", | |
| "else:\n", | |
| " dp_adapter = Adapter(312, 768)\n", | |
| " print('creating from scratch')\n", | |
| "dp_adapter.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "65697756d861478a9ac41147461c622f", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=642.0, style=ProgressStyle(description_…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "67a62027a8c1455d82757d280188ac28", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=711456784.0, style=ProgressStyle(descri…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "727455cc6bac4c7aab2408c07f1c7de3", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1649718.0, style=ProgressStyle(descript…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "339c4748dc4b4d8baf2f485693c96b43", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "77c749bd8cf64202b8316c005d142a81", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=24.0, style=ProgressStyle(description_w…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "loading\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "qZUYiy8tLDOe" | |
| }, | |
| "source": [ | |
| "def get_rubert_loss(outputs_list, texts):\n", | |
| " inp = {k: v.to(rubert.device) for k, v in rubert_tokenizer(texts, return_tensors='pt', padding=True, max_length=512, truncation=True).items()}\n", | |
| " with torch.no_grad():\n", | |
| " dp_out = rubert(**inp)\n", | |
| " emb = torch.nn.functional.normalize(dp_out.last_hidden_state[:, 0, :]) # pooler_output is worse for rubert\n", | |
| " lfun = torch.nn.MSELoss()\n", | |
| " loss = sum([\n", | |
| " lfun(torch.nn.functional.normalize(dp_adapter(out)), emb) \n", | |
| " for out in outputs_list\n", | |
| " ])\n", | |
| " return loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "b91ihgpvZ61G" | |
| }, | |
| "source": [ | |
| "from laserembeddings import Laser\n", | |
| "laser = Laser()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "AxduEcunaaeC", | |
| "outputId": "aaa05700-2385-4603-fa2d-645ac083927d" | |
| }, | |
| "source": [ | |
| "import os\n", | |
| "laser_adapter_path = '/gd/MyDrive/models/tinybert-ru-laser-adapter.pt'\n", | |
| "if os.path.exists(laser_adapter_path):\n", | |
| " laser_adapter = torch.load(laser_adapter_path)\n", | |
| " print('loading')\n", | |
| "else:\n", | |
| " laser_adapter = Adapter(312, 1024)\n", | |
| " print('creating from scratch')\n", | |
| "laser_adapter.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "loading\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "a9pGwp_lakBo" | |
| }, | |
| "source": [ | |
| "def get_laser_loss(outputs_list, texts, language='en'):\n", | |
| " with torch.no_grad():\n", | |
| " embeddings = laser.embed_sentences(texts, lang=language)\n", | |
| " emb = torch.nn.functional.normalize(torch.tensor(embeddings).to(outputs_list[0].device))\n", | |
| " lfun = torch.nn.MSELoss()\n", | |
| " loss = sum([\n", | |
| " lfun(torch.nn.functional.normalize(laser_adapter(out)), emb) \n", | |
| " for out in outputs_list\n", | |
| " ])\n", | |
| " return loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "nMqR4wgJtYnD" | |
| }, | |
| "source": [ | |
| "Use: see https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3\n", | |
| "\n", | |
| "У него сильно течёт память, поэтому, возможно, стоит отменить эту затею. Но я таки пытаюсь. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "CG2ng2D-qWoM", | |
| "outputId": "4ca89aec-b848-445b-9ca4-f590aad19d68" | |
| }, | |
| "source": [ | |
| "import tensorflow_hub\n", | |
| "import tensorflow_text\n", | |
| "import tensorflow as tf\n", | |
| "\n", | |
| "use = tensorflow_hub.load(\"https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3\")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:absl:Using /tmp/tfhub_modules to cache modules.\n", | |
| "INFO:absl:Downloading TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3'.\n", | |
| "INFO:absl:Downloaded https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3, Total size: 334.32MB\n", | |
| "INFO:absl:Downloaded TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3'.\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "NAPmv39KqyAa", | |
| "outputId": "a85b727c-321b-40c6-9cbd-f9228a33c16b" | |
| }, | |
| "source": [ | |
| "import os\n", | |
| "use_adapter_path = '/gd/MyDrive/models/tinybert-ru-use-adapter.pt'\n", | |
| "if os.path.exists(use_adapter_path):\n", | |
| " use_adapter = torch.load(use_adapter_path)\n", | |
| " print('loading')\n", | |
| "else:\n", | |
| " use_adapter = Adapter(312, 512)\n", | |
| " print('creating from scratch')\n", | |
| "use_adapter.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "loading\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "J9xPiyvArAX1" | |
| }, | |
| "source": [ | |
| "def get_use_loss(outputs_list, texts):\n", | |
| " emb = use(texts)\n", | |
| " emb = torch.nn.functional.normalize(torch.tensor(emb.numpy()).to(outputs_list[0].device))\n", | |
| " lfun = torch.nn.MSELoss()\n", | |
| " loss = sum([\n", | |
| " lfun(torch.nn.functional.normalize(use_adapter(out)), emb) \n", | |
| " for out in outputs_list\n", | |
| " ])\n", | |
| " return loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3edTW4WJ3YkD" | |
| }, | |
| "source": [ | |
| "#### Reconstruction loss (T5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "a6TRi3k43anU" | |
| }, | |
| "source": [ | |
| "from transformers import T5ForConditionalGeneration, T5Tokenizer\n", | |
| "\n", | |
| "t5_name = 'cointegrated/rut5-small'\n", | |
| "t5_name = '/gd/MyDrive/models/tinybert-ru-t5-decoder'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "XpUIi7EM9it6" | |
| }, | |
| "source": [ | |
| "t5 = T5ForConditionalGeneration.from_pretrained(t5_name)\n", | |
| "t5_tokenizer = T5Tokenizer.from_pretrained(t5_name)\n", | |
| "\n", | |
| "t5.train();\n", | |
| "t5.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "QdNGHAz5-DTy", | |
| "outputId": "5b7ec7b7-3d21-4b69-93a2-ffa54e24701b" | |
| }, | |
| "source": [ | |
| "import os\n", | |
| "t5_adapter_path = '/gd/MyDrive/models/tinybert-ru-t5-adapter.pt'\n", | |
| "t5_states = 10\n", | |
| "if os.path.exists(t5_adapter_path):\n", | |
| " t5_adapter = torch.load(t5_adapter_path)\n", | |
| " print('loading')\n", | |
| "else:\n", | |
| " t5_adapter = Adapter(312, 512 * t5_states)\n", | |
| " print('creating from scratch')\n", | |
| "t5_adapter.cuda();" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "loading\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "GEBTbOIm3sBi" | |
| }, | |
| "source": [ | |
| "def get_t5_loss(outputs, texts):\n", | |
| " t5_repr = [t5_adapter(outputs).reshape([outputs.shape[0], t5_states, 512])]\n", | |
| " targets = t5_tokenizer(texts, return_tensors='pt', padding=True, truncation=True, max_length=512)\n", | |
| " out = t5(\n", | |
| " encoder_outputs=t5_repr, \n", | |
| " labels=targets['input_ids'].to(t5.device), \n", | |
| " decoder_attention_mask=targets['attention_mask'].to(t5.device),\n", | |
| " )\n", | |
| " return out.loss" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "J04PZMnAG_Pl" | |
| }, | |
| "source": [ | |
| "```\n", | |
| "labse_adapter = Adapter(312, 768)\n", | |
| "dp_adapter = Adapter(312, 768)\n", | |
| "laser_adapter = Adapter(312, 1024)\n", | |
| "t5_adapter = Adapter(312, 512 * t5_states)\n", | |
| "\n", | |
| "labse_adapter.cuda(), dp_adapter.cuda(), laser_adapter.cuda(), t5_adapter.cuda();\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cJaAefByCUbT" | |
| }, | |
| "source": [ | |
| "#### Training loop" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1fNhssU2Zujy" | |
| }, | |
| "source": [ | |
| "def cleanup():\n", | |
| " gc.collect()\n", | |
| " torch.cuda.empty_cache()\n", | |
| " tf.keras.backend.clear_session()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "vVrAGHiNLQzh", | |
| "outputId": "c9ce4826-9c1e-427a-a3d8-90765fb6c6a7" | |
| }, | |
| "source": [ | |
| "from itertools import chain\n", | |
| "optimizer = torch.optim.Adam(\n", | |
| " params=[p for p in chain(\n", | |
| " model.parameters(), \n", | |
| " t5.decoder.parameters(), t5.lm_head.parameters(),\n", | |
| " t5_adapter.parameters(),\n", | |
| " labse_adapter.parameters(),\n", | |
| " dp_adapter.parameters(),\n", | |
| " laser_adapter.parameters(),\n", | |
| " use_adapter.parameters(),\n", | |
| " ) if p.requires_grad], \n", | |
| " lr=1e-5 # larger learning rate is detrimental\n", | |
| ")\n", | |
| "len(optimizer.param_groups)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 47 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BBE1oqI_XDd9" | |
| }, | |
| "source": [ | |
| "optimizer.param_groups[0]['lr'] = 1e-5" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UZ5h6YaWWorm" | |
| }, | |
| "source": [ | |
| "scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=optimizer, T_0=1765)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YkaATwqq9WZx" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1x94DPaIdBEC" | |
| }, | |
| "source": [ | |
| "from tensorflow.errors import ResourceExhaustedError" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000, | |
| "referenced_widgets": [ | |
| "e5c01ec834474e8aa33b26947804e512", | |
| "2b62264fbffc4822864b3399614ec73e", | |
| "3d86795f60d44a508aff4f81ff3fe3ce", | |
| "2309468a56b747bfbf1929d47c08731b", | |
| "dd843dae9dd74a2ba1ec1d171f4a5dfe", | |
| "cd06b4930112436980d8ad761384f2a6", | |
| "d4dc016b50404879a30c90770e9ef415", | |
| "2bf4ad78caba457baff8347da6058746" | |
| ] | |
| }, | |
| "id": "KD3Ob-bBy1iQ", | |
| "outputId": "31663e41-9cc6-4a5e-c9af-0ade6e79b43c" | |
| }, | |
| "source": [ | |
| "batch_size = 16 # the size of 4 seems to be the limit on my local device, while on colab 32 is OK\n", | |
| "# with gpt on colab, 8 is maximum, or 16, with t5\n", | |
| "# when we do not distill any other models, batch size of 64 seems to be just fine (and 3 epochs promise to pass in less than 24 hours!)\n", | |
| "margin = 0.3\n", | |
| "temp = 3.0\n", | |
| "hard_freq = 0\n", | |
| "accumulation_steps = 4 # эта штука реально помогает, когда обучение подзастряло. А ещё ускоряет!\n", | |
| "\n", | |
| "epochs = 3\n", | |
| "save_steps = int(8192 / batch_size)\n", | |
| "window = int(1024 / batch_size * 4)\n", | |
| "print('window steps', window, 'save steps', save_steps)\n", | |
| "ewms = [0] * 20\n", | |
| "\n", | |
| "tq = trange(int(df_ru.shape[0] * epochs / batch_size))\n", | |
| "cleanup()\n", | |
| "\n", | |
| "model.train()\n", | |
| "t5.train()\n", | |
| "labse.train()\n", | |
| "#big_model.train()\n", | |
| "rubert.train()\n", | |
| "\n", | |
| "for i in tq:\n", | |
| " if hard_freq and i % hard_freq == 0:\n", | |
| " bb = df_ru.text.loc[hard_batch(batch_size)]\n", | |
| " else:\n", | |
| " bb = df_ru.text.sample(batch_size)\n", | |
| " eb = df_en.iloc[bb.index].text\n", | |
| "\n", | |
| " try:\n", | |
| " inputs_ru = preprocess_inputs(tokenizer(bb.tolist(), return_tensors='pt', padding=True, truncation=True))\n", | |
| " inputs_en = preprocess_inputs(tokenizer(eb.tolist(), return_tensors='pt', padding=True, truncation=True))\n", | |
| " outputs_ru = model(**inputs_ru, output_hidden_states=True)\n", | |
| " outputs_en = model(**inputs_en, output_hidden_states=True)\n", | |
| " pool_ru = pool(model, outputs_ru)\n", | |
| " pool_en = pool(model, outputs_en)\n", | |
| " \n", | |
| " losses = [\n", | |
| " sum([\n", | |
| " get_labse_loss([pool_ru, pool_en], eb.tolist()) * 768, \n", | |
| " get_rubert_loss([pool_ru, pool_en], bb.tolist()) * 768,\n", | |
| " get_laser_loss([pool_ru, pool_en], eb.tolist()) * 1024,\n", | |
| " get_use_loss([pool_ru, pool_en], eb.tolist()) * 512, \n", | |
| " get_use_loss([pool_ru, pool_en], bb.tolist()) * 512,\n", | |
| " ]),\n", | |
| " (get_t5_loss(pool_ru, bb.tolist()) + get_t5_loss(pool_en, bb.tolist())) * 1,\n", | |
| " #(distill(inputs_ru, outputs_ru, temperature=temp) + distill(inputs_en, outputs_en, temperature=temp)) * 25,\n", | |
| " get_mlm_loss(inputs_ru, outputs_ru) + get_mlm_loss(inputs_en, outputs_en),\n", | |
| " get_sentence_loss(pool_ru, pool_en, margin=margin),\n", | |
| " ]\n", | |
| " loss = sum(losses)\n", | |
| " loss.backward()\n", | |
| "\n", | |
| " except (RuntimeError, ResourceExhaustedError) as e:\n", | |
| " print('runtime error on batch', i, e)\n", | |
| " inputs_ru, inputs_en = None, None\n", | |
| " outputs_ru, outputs_en = None, None\n", | |
| " pool_ru, pool_en = None, None\n", | |
| " losses = None\n", | |
| " loss = None\n", | |
| " cleanup()\n", | |
| " tf.keras.backend.clear_session()\n", | |
| " continue\n", | |
| "\n", | |
| " w = 1 / min(i+1, window)\n", | |
| " ewms = [ewm * (1-w) + loss.item() * w for ewm, loss in zip(ewms, [loss] + losses)]\n", | |
| " desc = 'loss: ' + ' '.join(['{:2.2f}'.format(l) for l in ewms]) + '|{:2.1e}'.format(optimizer.param_groups[0]['lr'])\n", | |
| " tq.set_description(desc)\n", | |
| "\n", | |
| " if i % accumulation_steps == 0:\n", | |
| " optimizer.step()\n", | |
| " scheduler.step()\n", | |
| " \n", | |
| " optimizer.zero_grad()\n", | |
| " tf.keras.backend.clear_session()\n", | |
| " cleanup()\n", | |
| " \n", | |
| " if i % window == 0 and i > 0:\n", | |
| " print(desc)\n", | |
| " # cleanup()\n", | |
| "\n", | |
| " if i % save_steps == 0 and i > 0:\n", | |
| " model.save_pretrained(NEW_MODEL_NAME)\n", | |
| " tokenizer.save_pretrained(NEW_MODEL_NAME)\n", | |
| " t5_tokenizer.save_pretrained(t5_name)\n", | |
| " t5.save_pretrained(t5_name)\n", | |
| " torch.save(labse_adapter, ladapter_path)\n", | |
| " torch.save(dp_adapter, dpadapter_path)\n", | |
| " torch.save(t5_adapter, t5_adapter_path)\n", | |
| " torch.save(laser_adapter, laser_adapter_path)\n", | |
| " torch.save(use_adapter, use_adapter_path)\n", | |
| " print('saving...', i, optimizer.param_groups[0]['lr'])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "window steps 256 save steps 512\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "e5c01ec834474e8aa33b26947804e512", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, max=471411.0), HTML(value='')))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "runtime error on batch 121 CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 15.90 GiB total capacity; 13.54 GiB already allocated; 31.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.22 6.77 2.09 7.74 4.62|9.8e-06\n", | |
| "loss: 21.16 6.81 2.06 7.67 4.62|9.6e-06\n", | |
| "saving... 512 9.572540863471041e-06\n", | |
| "loss: 21.15 6.80 2.06 7.66 4.63|9.3e-06\n", | |
| "loss: 21.15 6.79 2.06 7.67 4.62|9.0e-06\n", | |
| "saving... 1024 8.997487975927483e-06\n", | |
| "runtime error on batch 1108 CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 15.90 GiB total capacity; 13.45 GiB already allocated; 27.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.18 6.80 2.05 7.70 4.63|8.6e-06\n", | |
| "runtime error on batch 1408 CUDA out of memory. Tried to allocate 406.00 MiB (GPU 0; 15.90 GiB total capacity; 12.87 GiB already allocated; 415.75 MiB free; 13.46 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 1428 CUDA out of memory. Tried to allocate 96.00 MiB (GPU 0; 15.90 GiB total capacity; 13.46 GiB already allocated; 115.75 MiB free; 13.76 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 1492 CUDA out of memory. Tried to allocate 62.00 MiB (GPU 0; 15.90 GiB total capacity; 13.66 GiB already allocated; 53.75 MiB free; 13.82 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.19 6.83 2.04 7.69 4.64|8.2e-06\n", | |
| "saving... 1536 8.243008619517074e-06\n", | |
| "runtime error on batch 1599 CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 15.90 GiB total capacity; 13.63 GiB already allocated; 25.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 1765 CUDA out of memory. Tried to allocate 408.00 MiB (GPU 0; 15.90 GiB total capacity; 12.91 GiB already allocated; 413.75 MiB free; 13.46 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.17 6.82 2.05 7.67 4.63|7.8e-06\n", | |
| "loss: 21.24 6.81 2.05 7.76 4.63|7.3e-06\n", | |
| "saving... 2048 7.299638038632134e-06\n", | |
| "runtime error on batch 2071 CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 15.90 GiB total capacity; 13.36 GiB already allocated; 225.75 MiB free; 13.65 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 2248 CUDA out of memory. Tried to allocate 406.00 MiB (GPU 0; 15.90 GiB total capacity; 13.10 GiB already allocated; 315.75 MiB free; 13.56 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.16 6.82 2.05 7.67 4.62|6.8e-06\n", | |
| "loss: 21.14 6.80 2.04 7.67 4.62|6.3e-06\n", | |
| "saving... 2560 6.246035287102069e-06\n", | |
| "runtime error on batch 2816 CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 15.90 GiB total capacity; 13.51 GiB already allocated; 21.75 MiB free; 13.85 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 2919 CUDA out of memory. Tried to allocate 630.00 MiB (GPU 0; 15.90 GiB total capacity; 12.94 GiB already allocated; 357.75 MiB free; 13.52 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 3030 CUDA out of memory. Tried to allocate 14.00 MiB (GPU 0; 15.90 GiB total capacity; 13.54 GiB already allocated; 27.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.24 6.80 2.05 7.76 4.62|5.1e-06\n", | |
| "saving... 3072 5.12903126462487e-06\n", | |
| "runtime error on batch 3136 CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 15.90 GiB total capacity; 13.28 GiB already allocated; 31.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 3292 CUDA out of memory. Tried to allocate 556.00 MiB (GPU 0; 15.90 GiB total capacity; 12.37 GiB already allocated; 455.75 MiB free; 13.42 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 3302 2 root error(s) found.\n", | |
| " (0) Resource exhausted: OOM when allocating tensor with shape[16,8,1018,1018] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", | |
| "\t [[{{node StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/SparseTransformerEncode/Layer_0/SelfAttention/SparseMultiheadAttention/DotProductAttention/MatMul}}]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| "\t [[StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/layer_prepostprocess/layer_norm/add_1/_128]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| " (1) Resource exhausted: OOM when allocating tensor with shape[16,8,1018,1018] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", | |
| "\t [[{{node StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/SparseTransformerEncode/Layer_0/SelfAttention/SparseMultiheadAttention/DotProductAttention/MatMul}}]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| "0 successful operations.\n", | |
| "0 derived errors ignored. [Op:__inference_restored_function_body_40710]\n", | |
| "\n", | |
| "Function call stack:\n", | |
| "restored_function_body -> restored_function_body\n", | |
| "\n", | |
| "loss: 21.16 6.79 2.05 7.70 4.62|4.6e-06\n", | |
| "loss: 21.14 6.78 2.03 7.71 4.62|4.0e-06\n", | |
| "saving... 3584 4.014185256361644e-06\n", | |
| "loss: 21.08 6.79 2.00 7.67 4.62|3.5e-06\n", | |
| "runtime error on batch 4042 CUDA out of memory. Tried to allocate 96.00 MiB (GPU 0; 15.90 GiB total capacity; 13.74 GiB already allocated; 45.75 MiB free; 13.82 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.13 6.80 2.02 7.68 4.62|2.9e-06\n", | |
| "saving... 4096 2.932496871248491e-06\n", | |
| "loss: 21.04 6.78 2.00 7.63 4.62|2.4e-06\n", | |
| "runtime error on batch 4482 CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 15.90 GiB total capacity; 13.39 GiB already allocated; 33.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.12 6.77 2.05 7.67 4.62|2.0e-06\n", | |
| "saving... 4608 1.9576640232531785e-06\n", | |
| "runtime error on batch 4620 CUDA out of memory. Tried to allocate 80.00 MiB (GPU 0; 15.90 GiB total capacity; 13.56 GiB already allocated; 27.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 4727 CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 15.90 GiB total capacity; 13.24 GiB already allocated; 313.75 MiB free; 13.56 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 4761 CUDA out of memory. Tried to allocate 410.00 MiB (GPU 0; 15.90 GiB total capacity; 13.10 GiB already allocated; 317.75 MiB free; 13.56 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 4767 CUDA out of memory. Tried to allocate 440.00 MiB (GPU 0; 15.90 GiB total capacity; 13.45 GiB already allocated; 139.75 MiB free; 13.73 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.11 6.79 2.05 7.64 4.62|1.5e-06\n", | |
| "loss: 21.10 6.78 2.05 7.66 4.62|1.2e-06\n", | |
| "saving... 5120 1.1457324715546463e-06\n", | |
| "runtime error on batch 5237 CUDA out of memory. Tried to allocate 386.00 MiB (GPU 0; 15.90 GiB total capacity; 13.05 GiB already allocated; 289.75 MiB free; 13.59 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.17 6.81 2.07 7.67 4.62|8.1e-07\n", | |
| "runtime error on batch 5503 CUDA out of memory. Tried to allocate 24.00 MiB (GPU 0; 15.90 GiB total capacity; 13.59 GiB already allocated; 25.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.23 6.84 2.07 7.68 4.63|5.3e-07\n", | |
| "saving... 5632 5.259350417824765e-07\n", | |
| "loss: 21.15 6.81 2.02 7.69 4.63|3.0e-07\n", | |
| "runtime error on batch 6064 CUDA out of memory. Tried to allocate 444.00 MiB (GPU 0; 15.90 GiB total capacity; 13.11 GiB already allocated; 399.75 MiB free; 13.48 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 6102 CUDA out of memory. Tried to allocate 580.00 MiB (GPU 0; 15.90 GiB total capacity; 13.11 GiB already allocated; 457.75 MiB free; 13.42 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.21 6.81 2.03 7.73 4.63|1.4e-07\n", | |
| "saving... 6144 1.3945190165475963e-07\n", | |
| "runtime error on batch 6212 2 root error(s) found.\n", | |
| " (0) Resource exhausted: OOM when allocating tensor with shape[16,8,1312,1312] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", | |
| "\t [[{{node StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/SparseTransformerEncode/Layer_0/SelfAttention/SparseMultiheadAttention/DotProductAttention/MatMul}}]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| "\t [[StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/layer_prepostprocess/layer_norm/add_1/_128]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| " (1) Resource exhausted: OOM when allocating tensor with shape[16,8,1312,1312] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", | |
| "\t [[{{node StatefulPartitionedCall/StatefulPartitionedCall/EncoderTransformer/Transformer/SparseTransformerEncode/Layer_0/SelfAttention/SparseMultiheadAttention/DotProductAttention/MatMul}}]]\n", | |
| "Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", | |
| "\n", | |
| "0 successful operations.\n", | |
| "0 derived errors ignored. [Op:__inference_restored_function_body_40710]\n", | |
| "\n", | |
| "Function call stack:\n", | |
| "restored_function_body -> restored_function_body\n", | |
| "\n", | |
| "runtime error on batch 6281 CUDA out of memory. Tried to allocate 598.00 MiB (GPU 0; 15.90 GiB total capacity; 13.23 GiB already allocated; 415.75 MiB free; 13.46 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.14 6.81 1.99 7.70 4.63|4.0e-08\n", | |
| "runtime error on batch 6416 CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 15.90 GiB total capacity; 13.43 GiB already allocated; 27.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 6583 CUDA out of memory. Tried to allocate 96.00 MiB (GPU 0; 15.90 GiB total capacity; 13.65 GiB already allocated; 37.75 MiB free; 13.83 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.13 6.80 2.02 7.69 4.62|5.1e-10\n", | |
| "saving... 6656 3.880974563125195e-10\n", | |
| "runtime error on batch 6665 CUDA out of memory. Tried to allocate 440.00 MiB (GPU 0; 15.90 GiB total capacity; 13.46 GiB already allocated; 87.75 MiB free; 13.78 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 6841 CUDA out of memory. Tried to allocate 48.00 MiB (GPU 0; 15.90 GiB total capacity; 13.45 GiB already allocated; 61.75 MiB free; 13.81 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.14 6.82 2.05 7.65 4.63|1.0e-05\n", | |
| "runtime error on batch 6989 CUDA out of memory. Tried to allocate 388.00 MiB (GPU 0; 15.90 GiB total capacity; 13.52 GiB already allocated; 67.75 MiB free; 13.80 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.19 6.82 2.11 7.64 4.63|9.9e-06\n", | |
| "saving... 7168 9.884484122057703e-06\n", | |
| "runtime error on batch 7192 CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 15.90 GiB total capacity; 13.64 GiB already allocated; 21.75 MiB free; 13.85 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.24 6.83 2.11 7.68 4.63|9.7e-06\n", | |
| "loss: 21.19 6.82 2.10 7.65 4.63|9.5e-06\n", | |
| "saving... 7680 9.520719083212402e-06\n", | |
| "runtime error on batch 7914 CUDA out of memory. Tried to allocate 74.00 MiB (GPU 0; 15.90 GiB total capacity; 13.47 GiB already allocated; 57.75 MiB free; 13.81 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.08 6.77 2.02 7.66 4.63|9.3e-06\n", | |
| "runtime error on batch 8134 CUDA out of memory. Tried to allocate 454.00 MiB (GPU 0; 15.90 GiB total capacity; 13.46 GiB already allocated; 97.75 MiB free; 13.77 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.16 6.80 2.06 7.67 4.63|8.9e-06\n", | |
| "saving... 8192 8.921413801302305e-06\n", | |
| "runtime error on batch 8237 CUDA out of memory. Tried to allocate 54.00 MiB (GPU 0; 15.90 GiB total capacity; 13.70 GiB already allocated; 31.75 MiB free; 13.84 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 8399 CUDA out of memory. Tried to allocate 442.00 MiB (GPU 0; 15.90 GiB total capacity; 13.26 GiB already allocated; 267.75 MiB free; 13.61 GiB reserved in total by PyTorch)\n", | |
| "runtime error on batch 8429 CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 15.90 GiB total capacity; 13.47 GiB already allocated; 39.75 MiB free; 13.83 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.09 6.78 2.05 7.64 4.62|8.5e-06\n", | |
| "loss: 21.09 6.78 2.04 7.65 4.62|8.1e-06\n", | |
| "saving... 8704 8.119436623919674e-06\n", | |
| "runtime error on batch 8845 CUDA out of memory. Tried to allocate 574.00 MiB (GPU 0; 15.90 GiB total capacity; 12.96 GiB already allocated; 587.75 MiB free; 13.29 GiB reserved in total by PyTorch)\n", | |
| "loss: 21.09 6.79 2.03 7.66 4.61|7.7e-06\n", | |
| "loss: 21.12 6.79 2.03 7.68 4.62|7.2e-06\n", | |
| "saving... 9216 7.156236437334013e-06\n", | |
| "loss: 21.20 6.81 2.03 7.74 4.63|6.6e-06\n", | |
| "loss: 21.17 6.81 2.07 7.67 4.63|6.1e-06\n", | |
| "saving... 9728 6.081594676871018e-06\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "error", | |
| "ename": "KeyboardInterrupt", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-54-212e7cfcf72f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0minputs_ru\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0minputs_en\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0moutputs_ru\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs_ru\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m<ipython-input-26-4c51bd26b02f>\u001b[0m in \u001b[0;36mpreprocess_inputs\u001b[0;34m(inputs)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpreprocess_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'labels'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_mask_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m<ipython-input-25-586a2ede563c>\u001b[0m in \u001b[0;36mget_mask_labels\u001b[0;34m(input_ids)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mref_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_id_to_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mref_tokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 589\u001b[0m \u001b[0;34m'iterations executed (and might lead to errors or silently give '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 590\u001b[0m 'incorrect results).', category=RuntimeWarning)\n\u001b[0;32m--> 591\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 593\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m: " | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "X25iO3ckbtAb" | |
| }, | |
| "source": [ | |
| "Семантика предложений в текстах, которые восстановил T5, угадывается очень-очень приблизительно. \n", | |
| "\n", | |
| "Но угадывается именно семантика, что приятно. \n", | |
| "\n", | |
| "А вообще я, конечно, дурак. CLS-токены надо было сразу из LABSE дистиллировать. К чему я в итоге и пришёл, и представительная сила CLS заметно возросла (и даже спустя время стала выше, чем у усреднения эмбеддингов всех токенов). \n", | |
| "\n", | |
| "~~Долбаный T5 я отключу, он ничему так и не научился, а GPU выжирает сильно. Так я хоть батчи смогу побольше сделать. ~~ Включил назад, пусть будет. Поменял адаптер только. И да, с более толстым адаптером (и с боле простыми примерами из татоебы) декодер учится заметно лучше. Когда лосс T5 около 2.2-2.3, он уже неплохо декодирует некоторые предложения (короткие иногда даже полностью). \n", | |
| "\n", | |
| "Пробовал отключить \"учителей\" от обучения, но качество CLS токена практически моментально упало с 0.58 до 0.56 на детекции парафраз (а потом до 0.54), и я подумал, что нафиг такие эксперименты. \n", | |
| "\n", | |
| "А вот отключение bert-multilingual для дистилляции токенов сильно помогло - начал уменьшаться лосс как для CLS, так и для MLM. Видимо, плохой учитель в какой-то момент начинает вредить. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "4ria8f56NO-T", | |
| "outputId": "34b87e8d-359a-4278-f33d-0c9598e594a5" | |
| }, | |
| "source": [ | |
| "from transformers.modeling_outputs import BaseModelOutput\n", | |
| "model.eval()\n", | |
| "t5.eval()\n", | |
| "\n", | |
| "def decipher(text, **kwargs):\n", | |
| " bert_in = {k: v.to(model.device) for k, v in tokenizer(text, return_tensors='pt').items()}\n", | |
| " \n", | |
| " with torch.no_grad():\n", | |
| " bert_out = model(**bert_in, output_hidden_states=True)\n", | |
| " pooled = pool(model, bert_out)\n", | |
| " eo = BaseModelOutput(last_hidden_state=t5_adapter(pooled).reshape([pooled.shape[0], t5_states, 512]))\n", | |
| "\n", | |
| " out = t5.generate(encoder_outputs=eo, **kwargs)\n", | |
| " return t5_tokenizer.decode(out[0])\n", | |
| "\n", | |
| "for text in df_en.text.sample(5):\n", | |
| " print(text)\n", | |
| " print(decipher(text, max_length=64, repetition_penalty=3.0, num_beams=3))\n", | |
| " print()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "If you j...\n", | |
| "<pad> Если ты...</s>\n", | |
| "\n", | |
| "My delegation encourages the CTC in its efforts to intensify its cooperation with international, regional and subregional organizations.\n", | |
| "<pad> Япония приветствует усилия, направленные на содействие сотрудничеству между ЮНЕСКО и другими региональными организациями в области развития.</s>\n", | |
| "\n", | |
| "Don't look so dismal, Arthur.\n", | |
| "<pad> Не смотри, не беспокойся.</s>\n", | |
| "\n", | |
| "And Utanka took them back.\n", | |
| "<pad> И они пошли в Бостон.</s>\n", | |
| "\n", | |
| "I was in love with Tom once.\n", | |
| "<pad> Я был влюблён в Тома.</s>\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "kDFFPkAXWGma", | |
| "outputId": "2dc6392f-5bc5-45cc-da2d-3dc4dbe0ff68" | |
| }, | |
| "source": [ | |
| "for text in df_ru.text.sample(5):\n", | |
| " print(text)\n", | |
| " print(decipher(text, max_length=64, repetition_penalty=3.0, num_beams=3))\n", | |
| " print()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Я знаком с теми женщинами.\n", | |
| "<pad> Я знаком с ней с мужчинами.</s>\n", | |
| "\n", | |
| "Знаете, я никогда не участвовала в соревнованиях для инвалидов, я всегда выступала против здоровых спортсменов.\n", | |
| "<pad> Я никогда не играл в спортивных клубах, но всегда я люблю участвовать в спорте.</s>\n", | |
| "\n", | |
| "3.4 Сервис обязуется использовать только те букмекерские конторы, в которых был размещен по его рекомендации игровой банк Подписчика.\n", | |
| "<pad> 2. Для этого пользователь должен использовать в качестве приложения, который используется для использования на основе списка клиентов.</s>\n", | |
| "\n", | |
| "Для того чтобы понять, насколько оправданно введение моратория и на чьей стороне правда: то ли на стороне экологов, выступающих против отлова, то ли сотрудников дельфинариев, утверждающих, что запрет 2008 года никоим образом не способствует спасению популяций дельфинов, мы обратились за разъяснениями к преподавателю кафедры зоологии Таврического национального университета имени Вернадского Павлу Гольдину.\n", | |
| "<pad> Поскольку мы не считаем, что этот вопрос по-прежнему приводит к убеждению в том, что отсутствие прав человека на протяжении последнего десятилетия до тех пор, пока нет необходимости сказать, что из-за того, чтобы выясни\n", | |
| "\n", | |
| "Я понял.\n", | |
| "<pad> Я понял.</s>\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NEPxAvaRW7_g" | |
| }, | |
| "source": [ | |
| "Ниже - история значений лоссов за ход обучения. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "GzX2BGlVwCCt" | |
| }, | |
| "source": [ | |
| "```\n", | |
| "loss: 21.03 6.78 2.02 7.62 4.62\n", | |
| "loss: 21.11 6.81 2.03 7.64 4.63\n", | |
| "loss: 21.13 6.86 2.03 7.61 4.63\n", | |
| "loss: 21.15 6.88 2.03 7.62 4.62\n", | |
| "loss: 21.17 6.87 2.02 7.65 4.63\n", | |
| "loss: 21.25 6.89 2.01 7.71 4.63\n", | |
| "loss: 21.28 6.92 2.08 7.67 4.62\n", | |
| "loss: 21.30 6.93 2.08 7.66 4.63\n", | |
| "loss: 21.33 6.93 2.10 7.67 4.63\n", | |
| "loss: 21.36 6.99 2.05 7.69 4.64\n", | |
| "loss: 21.38 7.00 2.10 7.65 4.62\n", | |
| "loss: 21.44 7.04 2.12 7.65 4.64\n", | |
| "loss: 21.52 7.10 2.06 7.71 4.64\n", | |
| "# назад 16, ибо участились overflow\n", | |
| "loss: 22.15 7.09 1.91 7.73 5.43\n", | |
| "loss: 22.19 7.12 1.88 7.77 5.42\n", | |
| "loss: 22.24 7.14 1.91 7.77 5.43\n", | |
| "loss: 22.31 7.22 1.88 7.77 5.43\n", | |
| "loss: 22.35 7.24 1.89 7.79 5.43\n", | |
| "loss: 22.40 7.24 1.90 7.83 5.43\n", | |
| "loss: 22.64 7.32 1.95 7.93 5.43\n", | |
| "# сделал батч 24 вместо 16\n", | |
| "loss: 21.81 7.25 2.09 7.83 4.64\n", | |
| "# отключил в порядке эксперимента потокенную дистилляцию\n", | |
| "loss: 28.31 7.32 2.10 6.44 7.80 4.65\n", | |
| "loss: 28.37 7.33 2.07 6.47 7.85 4.65\n", | |
| "loss: 28.48 7.37 2.15 6.53 7.78 4.64\n", | |
| "loss: 28.53 7.40 2.12 6.51 7.85 4.64\n", | |
| "loss: 28.53 7.45 2.11 6.51 7.81 4.65\n", | |
| "loss: 28.57 7.52 2.11 6.51 7.79 4.65\n", | |
| "loss: 28.63 7.56 2.14 6.50 7.79 4.64 # или ломается T5?\n", | |
| "loss: 28.63 7.60 2.09 6.46 7.83 4.65\n", | |
| "# Добавил USE-multilingual-large в учителя, вроде ничего не ломается\n", | |
| "loss: 24.19 3.20 2.10 6.48 7.77 4.64\n", | |
| "loss: 24.24 3.20 2.12 6.46 7.80 4.65\n", | |
| "loss: 24.26 3.19 2.12 6.50 7.80 4.65\n", | |
| "loss: 24.29 3.21 2.16 6.54 7.74 4.64\n", | |
| "loss: 24.32 3.23 2.14 6.46 7.84 4.65\n", | |
| "loss: 24.36 3.24 2.13 6.49 7.84 4.66\n", | |
| "loss: 24.43 3.31 2.15 6.46 7.83 4.68\n", | |
| "# включил truncation в учителях, и сразу обучение как-то бодрее пошло. \n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ygHeduRu3C9B" | |
| }, | |
| "source": [ | |
| "```\n", | |
| "loss: 24.50 3.31 2.14 6.49 7.88 4.67\n", | |
| "loss: 24.53 3.31 2.17 6.50 7.87 4.68\n", | |
| "loss: 24.55 3.33 2.14 6.49 7.91 4.68\n", | |
| "loss: 24.64 3.35 2.16 6.55 7.89 4.69\n", | |
| "loss: 24.68 3.35 2.18 6.55 7.90 4.69\n", | |
| "loss: 24.71 3.37 2.19 6.51 7.94 4.70\n", | |
| "loss: 24.74 3.41 2.17 6.51 7.94 4.72\n", | |
| "loss: 24.85 3.42 2.22 6.52 7.96 4.72\n", | |
| "# Отключил \"сложные\" батчи, т.к. обучение стало довольно нестабильным, особенно по части T5, который, похоже, охуел от таких массовых градиентов. \n", | |
| "loss: 24.83 3.46 2.36 6.41 7.68 4.92\n", | |
| "loss: 24.86 3.48 2.38 6.35 7.71 4.94\n", | |
| "loss: 24.91 3.50 2.37 6.38 7.76 4.91\n", | |
| "loss: 25.07 3.56 2.39 6.37 7.81 4.95\n", | |
| "loss: 25.12 3.55 2.38 6.39 7.83 4.97\n", | |
| "loss: 25.17 3.61 2.43 6.42 7.75 4.97\n", | |
| "loss: 25.30 3.63 2.47 6.37 7.87 4.97\n", | |
| "# сделал каждый 4-й батч однородным (\"сложным\")\n", | |
| "loss: 25.24 3.79 2.16 6.47 7.95 4.86\n", | |
| "loss: 25.55 3.89 2.16 6.56 7.99 4.94\n", | |
| "# поправил индексацию обучающих данных, и внезапно оказались, что всё обстоит лучше с англо-русскими лоссами!\n", | |
| "loss: 26.82 3.70 2.27 7.26 8.03 5.56\n", | |
| "loss: 26.88 3.72 2.25 7.22 8.10 5.58\n", | |
| "loss: 26.91 3.71 2.23 7.23 8.15 5.58\n", | |
| "loss: 26.96 3.73 2.26 7.30 8.11 5.56\n", | |
| "loss: 27.01 3.73 2.27 7.30 8.13 5.57\n", | |
| "loss: 27.05 3.73 2.27 7.32 8.14 5.58\n", | |
| "loss: 27.19 3.80 2.35 7.31 8.14 5.58\n", | |
| "loss: 27.27 3.81 2.38 7.34 8.17 5.57\n", | |
| "loss: 27.38 3.82 2.39 7.37 8.21 5.58\n", | |
| "loss: 27.43 3.84 2.44 7.37 8.21 5.58\n", | |
| "loss: 27.46 3.84 2.43 7.37 8.23 5.59\n", | |
| "loss: 27.71 3.95 2.55 7.39 8.20 5.61\n", | |
| "loss: 27.75 3.95 2.55 7.40 8.22 5.62\n", | |
| "loss: 27.83 4.01 2.51 7.38 8.25 5.67\n", | |
| "loss: 27.99 4.05 2.53 7.42 8.29 5.69\n", | |
| "loss: 28.24 4.26 2.55 7.39 8.24 5.80\n", | |
| "loss: 28.81 4.72 2.58 7.45 8.29 5.77\n", | |
| "loss: 30.14 6.33 2.55 7.45 8.21 5.60\n", | |
| "# добавил в учителя ещё Laser, чтобы ещё лучше смысл предложения угадывался\n", | |
| "loss: 26.67 2.89 2.54 7.41 8.24 5.58\n", | |
| "loss: 26.93 2.92 2.77 7.39 8.26 5.59\n", | |
| "loss: 27.22 2.92 2.97 7.43 8.29 5.62 # when t5 loss gets lower than 2.5, I'll be satisfied\n", | |
| "loss: 27.83 2.93 3.47 7.54 8.27 5.61\n", | |
| "loss: 28.32 2.99 3.98 7.38 8.37 5.60\n", | |
| "# не понравилось, вернул T5 на место в новом формате\n", | |
| "loss: 25.31 2.91 7.20 8.23 6.96\n", | |
| "loss: 25.50 2.96 7.17 8.37 7.00\n", | |
| "# отключаю T5 нафиг, увеличиваю батч до 32\n", | |
| "loss: 48.62 2.95 24.28 7.46 8.32 5.59\n", | |
| "loss: 49.55 3.01 25.13 7.52 8.32 5.57\n", | |
| "# Удесятерил лосс от T5, ибо задолбало, что он совсем не учится\n", | |
| "loss: 26.91 3.03 2.51 7.46 8.32 5.60\n", | |
| "loss: 27.05 2.87 2.67 7.82 8.49 5.20\n", | |
| "## добавил tatoeba и opus100 корпус к яндексовскому, чтоб не учиться по кругу на одном и том же\n", | |
| "loss: 27.29 2.56 3.05 8.60 8.42 4.65\n", | |
| "loss: 27.34 2.58 3.06 8.64 8.41 4.66\n", | |
| "loss: 27.36 2.59 3.11 8.62 8.38 4.66\n", | |
| "loss: 27.43 2.62 3.07 8.64 8.43 4.67\n", | |
| "loss: 27.47 2.64 3.11 8.65 8.39 4.67\n", | |
| "loss: 27.57 2.66 3.10 8.69 8.44 4.68\n", | |
| "loss: 27.61 2.68 3.11 8.67 8.48 4.68\n", | |
| "loss: 27.65 2.76 3.07 8.65 8.45 4.72\n", | |
| "loss: 27.74 2.77 3.13 8.67 8.45 4.72\n", | |
| "loss: 27.83 2.81 3.16 8.67 8.43 4.75\n", | |
| "loss: 27.87 2.85 3.13 8.69 8.42 4.77\n", | |
| "loss: 28.38 3.11 3.13 8.73 8.50 4.91\n", | |
| "loss: 29.10 3.76 3.16 8.72 8.45 5.02\n", | |
| "# добавил rubert в дистилляцию (лосс сумируется с лоссом labse)\n", | |
| "loss: 26.81 1.86 3.12 8.74 8.44 4.65\n", | |
| "loss: 27.00 1.86 3.16 8.79 8.52 4.66\n", | |
| "# включил дропаут в моделях labse и big_model, шоб не скучно было!\n", | |
| "loss: 26.51 1.88 3.10 8.45 8.41 4.67\n", | |
| "loss: 26.55 1.88 3.12 8.48 8.43 4.65\n", | |
| "loss: 26.61 1.89 3.19 8.39 8.48 4.66\n", | |
| "loss: 27.37 2.20 3.18 8.76 8.49 4.76\n", | |
| "loss: 27.57 2.34 3.19 8.77 8.50 4.77\n", | |
| "loss: 27.74 2.47 3.12 8.83 8.54 4.78\n", | |
| "loss: 27.94 2.62 3.20 8.82 8.54 4.77\n", | |
| "loss: 28.11 2.81 3.17 8.89 8.48 4.76\n", | |
| "loss: 28.17 2.81 3.22 8.89 8.49 4.75\n", | |
| "loss: 28.32 2.81 3.22 8.98 8.55 4.76\n", | |
| "# поднял температуру дистилляции MLM до 3, а вес - до 35, ибо могу!\n", | |
| "loss: 21.25 2.82 3.18 2.08 8.41 4.76\n", | |
| "loss: 21.28 2.83 3.16 2.06 8.47 4.75\n", | |
| "loss: 21.31 2.84 3.22 2.08 8.42 4.75\n", | |
| "loss: 21.35 2.86 3.25 2.07 8.41 4.76\n", | |
| "loss: 21.38 2.86 3.21 2.08 8.48 4.76\n", | |
| "loss: 21.42 2.87 3.26 2.07 8.45 4.76\n", | |
| "loss: 21.49 2.89 3.27 2.09 8.48 4.75\n", | |
| "loss: 21.55 2.96 3.22 2.08 8.54 4.76\n", | |
| "loss: 22.57 3.38 3.42 2.13 8.66 4.99\n", | |
| "loss: 22.94 3.63 3.40 2.12 8.66 5.14\n", | |
| "loss: 23.12 3.84 3.33 2.12 8.65 5.18\n", | |
| "loss: 23.42 4.09 3.47 2.10 8.52 5.23\n", | |
| "loss: 23.55 4.11 3.42 2.11 8.68 5.22\n", | |
| "loss: 23.69 4.25 3.36 2.13 8.72 5.24\n", | |
| "loss: 23.82 4.28 3.36 2.13 8.81 5.24\n", | |
| "loss: 23.93 4.42 3.40 2.11 8.77 5.24\n", | |
| "loss: 28.08 8.75 3.52 2.11 8.79 4.91\n", | |
| "# (above: added LABSE loss to the front, en and ru T5 losses added together, everything disrupted).\n", | |
| "loss: 18.77 1.66 1.66 2.10 8.68 4.67\n", | |
| "loss: 18.82 1.69 1.70 2.09 8.68 4.66\n", | |
| "loss: 18.92 1.69 1.70 2.12 8.73 4.68\n", | |
| "loss: 19.00 1.79 1.80 2.12 8.61 4.69\n", | |
| "loss: 19.05 1.75 1.75 2.11 8.75 4.70\n", | |
| "loss: 19.19 1.78 1.78 2.13 8.81 4.70\n", | |
| "#\n", | |
| "loss: 19.28 1.87 1.87 2.12 8.73 4.70\n", | |
| "loss: 19.35 1.86 1.87 2.13 8.78 4.70\n", | |
| "loss: 19.36 1.91 1.91 2.14 8.69 4.71\n", | |
| "loss: 19.47 1.91 1.91 2.15 8.80 4.71\n", | |
| "loss: 19.95 2.17 2.17 2.11 8.81 4.69\n", | |
| "loss: 17.1293, 2.2217, 8.8566, 6.0510\n", | |
| "loss: 22.68 2.04 2.04 1.99 1.98 2.15 9.06 3.42\n", | |
| "loss: 22.14 1.94 1.95 1.91 1.90 2.13 8.91 3.40\n", | |
| "\n", | |
| "loss: 22.20 1.97 1.98 1.92 1.89 2.14 8.91 3.39\n", | |
| "loss: 22.52 2.00 2.01 1.95 1.92 2.15 9.08 3.42\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YK-1rjCT11Ef" | |
| }, | |
| "source": [ | |
| "## Publish the model to huggingface hub\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ecGBPcUc13oE" | |
| }, | |
| "source": [ | |
| "from transformers import BertForPreTraining, BertTokenizer\n", | |
| "NEW_MODEL_NAME = '/gd/MyDrive/models/tinybert-ru'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gWH6FcAK2Cu-" | |
| }, | |
| "source": [ | |
| "#model = BertForPreTraining.from_pretrained(NEW_MODEL_NAME)\n", | |
| "#tokenizer = BertTokenizerFast.from_pretrained(NEW_MODEL_NAME)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "tKhKlScE8Qm-" | |
| }, | |
| "source": [ | |
| "model.config.name_or_path = 'cointegrated/rubert-tiny'\n", | |
| "tokenizer.name_or_path = 'cointegrated/rubert-tiny'\n", | |
| "tokenizer.init_kwargs['name_or_path'] = 'cointegrated/rubert-tiny'" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "eas9bEos2SJr", | |
| "outputId": "3816197b-26cd-4b51-dba6-0108dfa11d7a" | |
| }, | |
| "source": [ | |
| "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n", | |
| "!sudo apt-get install git-lfs\n", | |
| "!git lfs install" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Detected operating system as Ubuntu/bionic.\n", | |
| "Checking for curl...\n", | |
| "Detected curl...\n", | |
| "Checking for gpg...\n", | |
| "Detected gpg...\n", | |
| "Running apt-get update... done.\n", | |
| "Installing apt-transport-https... done.\n", | |
| "Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n", | |
| "Importing packagecloud gpg key... done.\n", | |
| "Running apt-get update... done.\n", | |
| "\n", | |
| "The repository is setup! You can now install packages.\n", | |
| "Reading package lists... Done\n", | |
| "Building dependency tree \n", | |
| "Reading state information... Done\n", | |
| "The following NEW packages will be installed:\n", | |
| " git-lfs\n", | |
| "0 upgraded, 1 newly installed, 0 to remove and 48 not upgraded.\n", | |
| "Need to get 6,229 kB of archives.\n", | |
| "After this operation, 14.5 MB of additional disk space will be used.\n", | |
| "Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 2.13.3 [6,229 kB]\n", | |
| "Fetched 6,229 kB in 1s (7,087 kB/s)\n", | |
| "debconf: unable to initialize frontend: Dialog\n", | |
| "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n", | |
| "debconf: falling back to frontend: Readline\n", | |
| "debconf: unable to initialize frontend: Readline\n", | |
| "debconf: (This frontend requires a controlling tty.)\n", | |
| "debconf: falling back to frontend: Teletype\n", | |
| "dpkg-preconfigure: unable to re-open stdin: \n", | |
| "Selecting previously unselected package git-lfs.\n", | |
| "(Reading database ... 160776 files and directories currently installed.)\n", | |
| "Preparing to unpack .../git-lfs_2.13.3_amd64.deb ...\n", | |
| "Unpacking git-lfs (2.13.3) ...\n", | |
| "Setting up git-lfs (2.13.3) ...\n", | |
| "Git LFS initialized.\n", | |
| "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", | |
| "Git LFS initialized.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dCNFI0D02ZUP", | |
| "outputId": "46795703-dfd4-4210-eb3a-21defba0eacd" | |
| }, | |
| "source": [ | |
| "!pip install huggingface_hub" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.0.8)\n", | |
| "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.0.12)\n", | |
| "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (2.23.0)\n", | |
| "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.0.1)\n", | |
| "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.41.1)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2020.12.5)\n", | |
| "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2.10)\n", | |
| "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (1.24.3)\n", | |
| "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (3.0.4)\n", | |
| "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->huggingface_hub) (3.4.1)\n", | |
| "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->huggingface_hub) (3.7.4.3)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LL-NDqRC2gy8" | |
| }, | |
| "source": [ | |
| "!huggingface-cli login" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "bNKzawMF2h-9" | |
| }, | |
| "source": [ | |
| "# !huggingface-cli repo create rubert-tiny" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "xQunwCAIEe-S" | |
| }, | |
| "source": [ | |
| "! rm -rf rubert-tiny" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PR9uJT6CXUNS" | |
| }, | |
| "source": [ | |
| "В ячейке ниже может понадобиться ввести ваши логин и пароль, если будут проблемы с пушем" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dFNnZkKi3H-V", | |
| "outputId": "f566f517-6d4c-4764-ea4a-4413497582ce" | |
| }, | |
| "source": [ | |
| "!git clone https://huggingface.co/cointegrated/rubert-tiny" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Cloning into 'rubert-tiny'...\n", | |
| "remote: Enumerating objects: 64, done.\u001b[K\n", | |
| "remote: Counting objects: 100% (64/64), done.\u001b[K\n", | |
| "remote: Compressing objects: 100% (63/63), done.\u001b[K\n", | |
| "remote: Total 64 (delta 27), reused 0 (delta 0)\u001b[K\n", | |
| "Unpacking objects: 100% (64/64), done.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "n-y_dk2s3OVG", | |
| "outputId": "a3e06c51-6b99-4039-f607-23e571afb392" | |
| }, | |
| "source": [ | |
| "! cd rubert-tiny && git lfs install & git config --global user.email \"dale.david@mail.ru\"" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Updated git hooks.\n", | |
| "Git LFS initialized.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2HUe297y3RvQ" | |
| }, | |
| "source": [ | |
| "model.save_pretrained('rubert-tiny')\n", | |
| "#tokenizer.save_pretrained('rubert-tiny')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "mWFHsAoj3W5P", | |
| "outputId": "469f30e9-368c-4344-dc4b-899b6c4b2d4a" | |
| }, | |
| "source": [ | |
| "!ls -alsh rubert-tiny" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "total 47M\n", | |
| "4.0K drwxr-xr-x 3 root root 4.0K Jun 9 19:43 .\n", | |
| "4.0K drwxr-xr-x 1 root root 4.0K Jun 9 19:43 ..\n", | |
| "4.0K -rw-r--r-- 1 root root 632 Jun 9 19:43 config.json\n", | |
| "4.0K drwxr-xr-x 9 root root 4.0K Jun 9 19:43 .git\n", | |
| "4.0K -rw-r--r-- 1 root root 690 Jun 9 19:43 .gitattributes\n", | |
| " 46M -rw-r--r-- 1 root root 46M Jun 9 19:43 pytorch_model.bin\n", | |
| "4.0K -rw-r--r-- 1 root root 1.1K Jun 9 19:43 README.md\n", | |
| "4.0K -rw-r--r-- 1 root root 112 Jun 9 19:43 special_tokens_map.json\n", | |
| "4.0K -rw-r--r-- 1 root root 341 Jun 9 19:43 tokenizer_config.json\n", | |
| "460K -rw-r--r-- 1 root root 458K Jun 9 19:43 tokenizer.json\n", | |
| "236K -rw-r--r-- 1 root root 236K Jun 9 19:43 vocab.txt\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Zt_KdJvY9ZlM", | |
| "outputId": "6140e444-3497-4cc1-f40d-a580fb9c197a" | |
| }, | |
| "source": [ | |
| "! cd rubert-tiny && git status" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "On branch main\n", | |
| "Your branch is up to date with 'origin/main'.\n", | |
| "\n", | |
| "Changes not staged for commit:\n", | |
| " (use \"git add <file>...\" to update what will be committed)\n", | |
| " (use \"git checkout -- <file>...\" to discard changes in working directory)\n", | |
| "\n", | |
| "\t\u001b[31mmodified: pytorch_model.bin\u001b[m\n", | |
| "\n", | |
| "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "kVWQZH8P3aEs", | |
| "outputId": "5db702ae-3a38-41f5-f4c5-d3ae9d66bc8b" | |
| }, | |
| "source": [ | |
| "! cd rubert-tiny && git add . && git status" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "On branch main\n", | |
| "Your branch is up to date with 'origin/main'.\n", | |
| "\n", | |
| "Changes to be committed:\n", | |
| " (use \"git reset HEAD <file>...\" to unstage)\n", | |
| "\n", | |
| "\t\u001b[32mmodified: pytorch_model.bin\u001b[m\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Y2A1Kk049I3R", | |
| "outputId": "88e859cc-d4e4-4838-d988-5beb95b7e4df" | |
| }, | |
| "source": [ | |
| "! cd rubert-tiny && git diff --cached " | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[1mdiff --git a/pytorch_model.bin b/pytorch_model.bin\u001b[m\n", | |
| "\u001b[1mindex 9da29c2..eacb3f3 100644\u001b[m\n", | |
| "\u001b[1m--- a/pytorch_model.bin\u001b[m\n", | |
| "\u001b[1m+++ b/pytorch_model.bin\u001b[m\n", | |
| "\u001b[36m@@ -1,3 +1,3 @@\u001b[m\n", | |
| " version https://git-lfs.github.com/spec/v1\u001b[m\n", | |
| "\u001b[31m-oid sha256:bab658a6372a592efa5a8f6fde8149edf07f69e889d6c821a1e3fbf21cc82099\u001b[m\n", | |
| "\u001b[32m+\u001b[m\u001b[32moid sha256:7b46f70960011906bf9be3c46ad7490bada3845ddb5c2d7a8830c9517ee66071\u001b[m\n", | |
| " size 47679974\u001b[m\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "zzWincPK3e9B", | |
| "outputId": "2376737f-df38-4f05-b7dd-8c94a2eeb130" | |
| }, | |
| "source": [ | |
| "! cd rubert-tiny && git add . && git commit -m \"Train for a couple of days more\" && git push" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[main 0dd911d] Train for a couple of days more\n", | |
| " 1 file changed, 1 insertion(+), 1 deletion(-)\n", | |
| "Uploading LFS objects: 100% (1/1), 48 MB | 6.0 MB/s, done.\n", | |
| "Counting objects: 3, done.\n", | |
| "Delta compression using up to 2 threads.\n", | |
| "Compressing objects: 100% (3/3), done.\n", | |
| "Writing objects: 100% (3/3), 374 bytes | 374.00 KiB/s, done.\n", | |
| "Total 3 (delta 1), reused 0 (delta 0)\n", | |
| "To https://huggingface.co/cointegrated/rubert-tiny\n", | |
| " e032bcd..0dd911d main -> main\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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