{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "3FO5ESocXvlK" }, "source": [ "### **Data Preprocessing**\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "!pip install datasets\n", "!pip install transformers\n", "!pip install starcc\n", "!pip install sacrebleu" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'唔該你細聲d,我喺度做緊嘢,洗唔洗講呢d嘢。'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import defaultdict\n", "from random import randint, choice\n", "import math\n", "\n", "typo_table = defaultdict(list)\n", "\n", "with open(\"typos.txt\", \"r\") as typos_file:\n", " for line in typos_file.read().splitlines():\n", " [correct, typo] = line.split(\"\\t\")\n", " typo_table[correct].append(typo)\n", "\n", "def insert_typos(s: str) -> str:\n", " max_num_typos = randint(1, max(math.ceil(len(s) * 0.05), 1))\n", " num_typos = 0\n", " corrects = list(typo_table.keys())\n", " while num_typos < max_num_typos and len(corrects) > 0:\n", " correct = choice(corrects)\n", " corrects.remove(correct)\n", " typo = choice(typo_table[correct])\n", " if s.count(correct) > 0:\n", " num_typos += 1\n", " s = s.replace(correct, typo)\n", " return s\n", "\n", "insert_typos(\"唔該你細聲啲,我喺度做緊嘢,使唔使講呢啲嘢。\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Building prefix dict from the default dictionary ...\n", "Loading model from cache /var/folders/kk/n4ff6h1n3t170b1m4zv09yf40000gn/T/jieba.cache\n", "Loading model cost 0.339 seconds.\n", "Prefix dict has been built successfully.\n" ] } ], "source": [ "import json\n", "from StarCC import PresetConversion\n", "convert = PresetConversion(src='cn', dst='hk', with_phrase=False)\n", "\n", "with open(\"train/botisan.json\", \"r\") as botisan_file, open(\"train/tatoeba.tsv\", \"r\") as tatoeba_file, open(\"train/train.can\", \"w+\") as can_file, open(\"train/train.man\", \"w+\") as man_file:\n", " for line in botisan_file.readlines():\n", " translation = json.loads(line)[\"translation\"]\n", " can_file.write(translation[\"yue\"] + \"\\n\")\n", " man_file.write(convert(translation[\"zh\"]) + \"\\n\")\n", " can_file.write(insert_typos(translation[\"yue\"]) + \"\\n\")\n", " man_file.write(convert(translation[\"zh\"]) + \"\\n\")\n", " \n", " for line in tatoeba_file.read().splitlines():\n", " [_, can, _, man] = line.split(\"\\t\")\n", " can_file.write(can + \"\\n\")\n", " man_file.write(convert(man) + \"\\n\")\n", " can_file.write(insert_typos(can) + \"\\n\")\n", " man_file.write(convert(man) + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from os import listdir\n", "from os.path import isfile, join\n", "man_dir = \"train/man/\"\n", "can_dir = \"train/can/\"\n", "man_paths = [f for f in listdir(man_dir) if isfile(join(man_dir, f)) and f != \".DS_Store\"]\n", "with open(\"train/train.can\", \"a+\") as can_train_file, open(\"train/train.man\", \"a+\") as man_train_file:\n", " for man_path in man_paths:\n", " with open(man_dir + man_path, \"r\") as man_file:\n", " name = man_path[len(\"man-\"):-len(\".txt\")]\n", " can_path = \"can-\" + name + \".txt\"\n", " with open(can_dir + can_path, \"r\") as can_file:\n", " for man_line, can_line in zip(man_file.readlines(), can_file.readlines()):\n", " can_train_file.write(can_line.replace(\" \", \"\"))\n", " man_train_file.write(man_line.replace(\" \", \"\"))\n", " if name == \"novels\":\n", " can_train_file.write(insert_typos(can_line.replace(\" \", \"\")))\n", " man_train_file.write(insert_typos(man_line.replace(\" \", \"\")))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86f2f059a9804a309d034635efcf11e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Flattening the indices: 0%| | 0/135792 [00:00 [EOS]\n", " # cut off at BERT max length 512\n", " inputs = tokenizer(batch[\"can\"], padding=\"max_length\", truncation=True, max_length=128, return_tensors=\"pt\")\n", " input_ids = inputs.input_ids.to('mps')\n", " attention_mask = inputs.attention_mask.to('mps')\n", "\n", " outputs = model.generate(input_ids, attention_mask=attention_mask)\n", "\n", " # all special tokens including will be removed\n", " output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", " batch[\"pred\"] = [s.replace(\" \", \"\") for s in output_str]\n", " for man, pred in zip(batch[\"man\"], batch[\"pred\"]):\n", " pred_file.write(\"P: \" + pred + \"\\n\")\n", " pred_file.write(\"T: \" + man + \"\\n\\n\")\n", " return batch\n", "\n", "results = test_data.map(generate_summary, batched=True, batch_size=batch_size)\n", "\n", "pred_str = results[\"pred\"]\n", "label_str = results[\"man\"]\n", "\n", "# bleu_score = bleu.compute(predictions=pred_str, references=label_str, tokenizer=char_tokenizer)[\"bleu\"]\n", "# chrf_score = chrf.compute(predictions=pred_str, references=label_str)[\"score\"]\n", "\n", "bleu = sacrebleu.BLEU(trg_lang=\"zh\")\n", "bleu_score = bleu.corpus_score(label_str, [pred_str]).score\n", "chrf = sacrebleu.CHRF()\n", "chrf_score = chrf.corpus_score(label_str, [pred_str]).score\n", "\n", "print(\"BLEU score:\", bleu_score)\n", "print(\"CHRF score:\", chrf_score)\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "authorship_tag": "ABX9TyMYchE5beUGXDKxJoG2JsLp", "collapsed_sections": [], "include_colab_link": true, "name": "BERT2BERT for CNN/Dailymail", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, 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