diff --git "a/finetune.ipynb" "b/finetune.ipynb" new file mode 100644--- /dev/null +++ "b/finetune.ipynb" @@ -0,0 +1,5535 @@ +{ + "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" + ] + } + ], + 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