{ "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": [], "source": [ "import random\n", "\n", "random.seed(42)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'唔該你細聲啲，我喺度做緊野，使唔使講呢啲野。'" ] }, "execution_count": 2, "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": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded 177 normalization mappings\n" ] } ], "source": [ "abc_mapping = {}\n", "\n", "with open(\"abc_rare_char_mapping.txt\", \"r\") as input_file:\n", " for line in input_file.read().splitlines():\n", " [c, n, freq] = line.split(\"\\t\")\n", " if len(n) == 1:\n", " abc_mapping[c] = n\n", "\n", "print(\"Loaded {} normalization mappings\".format(len(abc_mapping)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# replace all occurence of rare characters with normalized ones\n", "def normalize_abc(line: str) -> str:\n", " for c, n in abc_mapping.items():\n", " line = line.replace(c, n)\n", " line = line.replace(\"而𠺢\", \"而家\").replace(\"依𠺢\", \"依家\")\n", " return line" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Normalize test and validation sets\n", "\n", "with open(\"para/test/test.can\", \"r\") as input_file:\n", " with open(\"para/test/test.norm.can\", \"w+\") as output_file:\n", " for line in input_file.read().splitlines():\n", " output_file.write(normalize_abc(line) + \"\\n\")\n", "\n", "with open(\"para/dev/dev.can\", \"r\") as input_file:\n", " with open(\"para/dev/dev.norm.can\", \"w+\") as output_file:\n", " for line in input_file.read().splitlines():\n", " output_file.write(normalize_abc(line) + \"\\n\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inserted typos into 4309 lines\n" ] } ], "source": [ "# Insert typos to test set\n", "num_typo_lines = 0\n", "with open(\"para/test/test.norm.can\", \"r\") as can_file, open(\"para/test/test.man\", \"r\") as man_file,\\\n", " open(\"para/test/test.typos.can\", \"w+\") as can_output,\\\n", " open(\"para/test/test.typos.man\", \"w+\") as man_output:\n", " for can_line, man_line in zip(can_file, man_file):\n", " can_line_with_typos = insert_typos(can_line)\n", " if can_line_with_typos != can_line:\n", " num_typo_lines += 1\n", " can_output.write(can_line_with_typos)\n", " man_output.write(man_line)\n", "\n", "print(f\"Inserted typos into {num_typo_lines} lines\")" ] }, { "cell_type": "code", "execution_count": 7, "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 0.364 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,\\\n", " 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", " if \"係度\" in translation[\"yue\"] or \"係到\" in translation[\"yue\"]: # This is a common typo that wasn't filtered out\n", " continue\n", " can = translation[\"yue\"].replace(\"⠀\", \"\").strip()\n", " man = convert(translation[\"zh\"].replace(\"⠀\", \"\").strip())\n", " can_file.write(can + \"\\n\")\n", " man_file.write(man + \"\\n\")\n", " can_typo = insert_typos(can)\n", " if can_typo != can:\n", " can_file.write(can_typo + \"\\n\")\n", " man_file.write(man + \"\\n\")\n", " \n", " for line in tatoeba_file.read().splitlines():\n", " [_, can, _, man] = line.split(\"\\t\")\n", " if \"係度\" in can or \"係到\" in can: # This is a common typo that wasn't filtered out\n", " continue\n", " can_file.write(can + \"\\n\")\n", " man_file.write(convert(man) + \"\\n\")\n", " can_typo = insert_typos(can)\n", " if can_typo != can:\n", " can_file.write(can_typo + \"\\n\")\n", " man_file.write(convert(man) + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "with open(\"train/train.can\", \"a+\") as can_train_file, open(\"train/train.man\", \"a+\") as man_train_file,\\\n", " open(\"commercial_baselines/bing.can\", \"r\") as can_bing_file, open(\"commercial_baselines/bing.man\", \"r\") as man_bing_file:\n", " for can_line, man_line in zip(can_bing_file.read().splitlines(), man_bing_file.read().splitlines()):\n", " can_train_file.write(can_line + \"\\n\")\n", " man_train_file.write(man_line + \"\\n\")\n", " can_typo = insert_typos(can_line)\n", " if can_typo != can_line:\n", " can_train_file.write(can_typo + \"\\n\")\n", " man_train_file.write(man_line + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "with open(\"train/train.can\", \"a+\") as can_train_file, open(\"train/train.man\", \"a+\") as man_train_file,\\\n", " open(\"train/lihkg.filtered.can\", \"r\") as can_lihkg_file, open(\"commercial_baselines/lihkg.filtered.man\", \"r\") as man_lihkg_file:\n", " for can_line, man_line in zip(can_lihkg_file.read().splitlines(), man_lihkg_file.read().splitlines()):\n", " if \"係度\" in can_line or \"係到\" in can_line: # This is a common typo that wasn't filtered out\n", " continue\n", " can_train_file.write(can_line + \"\\n\")\n", " man_train_file.write(man_line + \"\\n\")\n", " can_typo = insert_typos(can_line)\n", " if can_typo != can_line and random.random() < 0.2:\n", " can_train_file.write(can_typo + \"\\n\")\n", " man_train_file.write(man_line + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a114fbaaecac427a8bed6f7a7399081a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Flattening the indices: 0%| | 0/295518 [00:0027\u001b[0m \u001b[39m# instantiate trainer\u001b[39;00m\n\u001b[1;32m 28\u001b[0m trainer \u001b[39m=\u001b[39m Seq2SeqTrainer(\n\u001b[1;32m 29\u001b[0m model\u001b[39m=\u001b[39mbart,\n\u001b[1;32m 30\u001b[0m args\u001b[39m=\u001b[39mtraining_args,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 33\u001b[0m eval_dataset\u001b[39m=\u001b[39mval_data\u001b[39m.\u001b[39mselect(\u001b[39mrange\u001b[39m(batch_size \u001b[39m*\u001b[39m \u001b[39m10\u001b[39m)),\n\u001b[1;32m 34\u001b[0m )\n\u001b[0;32m---> 35\u001b[0m trainer\u001b[39m.\u001b[39;49mtrain()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/trainer.py:1633\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1628\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_wrapped \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 1630\u001b[0m inner_training_loop \u001b[39m=\u001b[39m find_executable_batch_size(\n\u001b[1;32m 1631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inner_training_loop, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_train_batch_size, args\u001b[39m.\u001b[39mauto_find_batch_size\n\u001b[1;32m 1632\u001b[0m )\n\u001b[0;32m-> 1633\u001b[0m \u001b[39mreturn\u001b[39;00m inner_training_loop(\n\u001b[1;32m 1634\u001b[0m args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 1635\u001b[0m resume_from_checkpoint\u001b[39m=\u001b[39;49mresume_from_checkpoint,\n\u001b[1;32m 1636\u001b[0m trial\u001b[39m=\u001b[39;49mtrial,\n\u001b[1;32m 1637\u001b[0m ignore_keys_for_eval\u001b[39m=\u001b[39;49mignore_keys_for_eval,\n\u001b[1;32m 1638\u001b[0m )\n", "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/trainer.py:1947\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1944\u001b[0m model\u001b[39m.\u001b[39mclip_grad_norm_(args\u001b[39m.\u001b[39mmax_grad_norm)\n\u001b[1;32m 1945\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1946\u001b[0m \u001b[39m# Revert to normal clipping otherwise, handling Apex or full precision\u001b[39;00m\n\u001b[0;32m-> 1947\u001b[0m nn\u001b[39m.\u001b[39;49mutils\u001b[39m.\u001b[39;49mclip_grad_norm_(\n\u001b[1;32m 1948\u001b[0m amp\u001b[39m.\u001b[39;49mmaster_params(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptimizer) \u001b[39mif\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49muse_apex \u001b[39melse\u001b[39;49;00m model\u001b[39m.\u001b[39;49mparameters(),\n\u001b[1;32m 1949\u001b[0m args\u001b[39m.\u001b[39;49mmax_grad_norm,\n\u001b[1;32m 1950\u001b[0m )\n\u001b[1;32m 1952\u001b[0m \u001b[39m# Optimizer step\u001b[39;00m\n\u001b[1;32m 1953\u001b[0m optimizer_was_run \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torch/nn/utils/clip_grad.py:82\u001b[0m, in \u001b[0;36mclip_grad_norm_\u001b[0;34m(parameters, max_norm, norm_type, error_if_nonfinite, foreach)\u001b[0m\n\u001b[1;32m 80\u001b[0m clip_coef_clamped_device \u001b[39m=\u001b[39m clip_coef_clamped\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m 81\u001b[0m \u001b[39mfor\u001b[39;00m g \u001b[39min\u001b[39;00m grads:\n\u001b[0;32m---> 82\u001b[0m g\u001b[39m.\u001b[39;49mdetach()\u001b[39m.\u001b[39;49mmul_(clip_coef_clamped_device)\n\u001b[1;32m 84\u001b[0m \u001b[39mreturn\u001b[39;00m total_norm\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# set training arguments - these params are not really tuned, feel free to change\n", "training_args = Seq2SeqTrainingArguments(\n", " output_dir=\"./\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " predict_with_generate=True,\n", " use_mps_device=True,\n", " do_train=True,\n", " evaluation_strategy=\"steps\",\n", " do_eval=True,\n", " logging_steps=200, # set to 1000 for full training\n", " save_steps=1000, # set to 500 for full training\n", " eval_steps=1000, # set to 8000 for full training\n", " warmup_steps=2000, # set to 2000 for full training\n", " overwrite_output_dir=True,\n", " save_total_limit=8,\n", " fp16=False,\n", " num_train_epochs=5,\n", ")\n", "\n", "# instantiate trainer\n", "trainer = Seq2SeqTrainer(\n", " model=bart,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=train_data,\n", " eval_dataset=val_data.select(range(batch_size * 10)),\n", ")\n", "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "ZwQIEhKOrJpl" }, "source": [ "### **Evaluation**\n", "\n", "Awesome, we finished training our dummy model. Let's now evaluated the model on the test data. We make use of the dataset's handy `.map()` function to generate a summary of each sample of the test data." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# map data correctly\n", "def generate_summary(model, pred_file, batch):\n", " # Tokenizer will automatically set [BOS] [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" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from transformers import BertTokenizer, BartForConditionalGeneration\n", "\n", "tokenizer = BertTokenizer.from_pretrained('Ayaka/bart-base-cantonese')\n", "model = BartForConditionalGeneration.from_pretrained(\"./checkpoint-11000\").to('mps')\n", "\n", "batch_size = 64 # change to 64 for full evaluation" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "26e7fd38a2404fdfa35cd85aeaed344e", "13600cf404f84084b6df2398b7b1546b", "2b3d36b270ec4a27a156a5aedd1e4a49", "97470b3ec2d545ea8c3bc464a44c6528", "a1efa9a3a6c6453782063039b65de800", "c453d3e11fd644d4a90372969fd4a1ec", "8f3600b5620b45b39d7e0b940f39960e", "a2ea693d77dd483e85604dcead910fc1" ] }, "id": "oOoSrwWarJAC", "outputId": "fdbf6e2a-ed91-4902-e1b8-5938311d3af3" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b8467d2e5fe8437dbe21676c93719c15", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/4309 [00:00 15\n", "Long BLEU: 0.0001133003812423589\n", "Long CHRF: 0.2785323244848631\n", "\n", "Base Phrase table: 80K\n", "Full BLEU: 29.764865629969385\n", "Full CHRF: 26.37265669702092\n", "Kept 1021 out of 6485 lines with length > 15\n", "Long BLEU: 30.332747246990724\n", "Long CHRF: 27.85622174467189\n", "\n", "Base Phrase table with typo augmentation: 130K\n", "Full BLEU: 29.6230711726356\n", "Full CHRF: 26.304954605724934\n", "Kept 1021 out of 6480 lines with length > 15\n", "Long BLEU: 30.161200446446298\n", "Long CHRF: 27.775833644294366\n", "\n", "Full Phrase table with typo augmentation: 130K\n", "Full BLEU: 28.735741304040417\n", "Full CHRF: 25.482035653550916\n", "Kept 1026 out of 6502 lines with length > 15\n", "Long BLEU: 29.470470986318464\n", "Long CHRF: 27.043012476120676\n", "\n", "Full Phrase table, bidirectional with typo augmentation: 175K\n", "Full BLEU: 28.96935496818149\n", "Full CHRF: 25.83725955850799\n", "Kept 1026 out of 6502 lines with length > 15\n", "Long BLEU: 29.550611277731083\n", "Long CHRF: 27.27031095261333\n", "\n", "Bing with typo augmentation: 300K\n", "Full BLEU: 37.92865291447074\n", "Full CHRF: 33.14536841535193\n", "Kept 1026 out of 6502 lines with length > 15\n", "Long BLEU: 37.374425001553085\n", "Long CHRF: 34.32181253307308\n" ] } ], "source": [ "import sacrebleu\n", "\n", "def eval_metrics(label_str, pred_str):\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", " return {\"bleu\": bleu_score, \"chrf\": chrf_score}\n", "\n", "def calculate_metrics(pred_file_path):\n", " # Normal eval\n", " pred_str = []\n", " label_str = []\n", " with open(pred_file_path, \"r\") as pred_file:\n", " pred_lines = pred_file.read().splitlines()\n", " for pred_line in pred_lines:\n", " if pred_line.startswith(\"P:\"):\n", " pred_str.append(pred_line.removeprefix(\"P: \"))\n", " elif pred_line.startswith(\"T:\"):\n", " label_str.append(pred_line.removeprefix(\"T: \"))\n", "\n", " full_metrics = eval_metrics(label_str, pred_str)\n", " print(f\"Full BLEU: {full_metrics['bleu']}\")\n", " print(f\"Full CHRF: {full_metrics['chrf']}\")\n", "\n", " # Length > 15\n", " pred_str = []\n", " label_str = []\n", " with open(pred_file_path, \"r\") as pred_file:\n", " pred_lines = pred_file.read().splitlines()\n", " i = 0\n", " num_bitext = 0\n", " while i < len(pred_lines):\n", " if pred_lines[i].startswith(\"P:\") and len(pred_lines[i + 1]) - 3 <= 15:\n", " num_bitext += 1\n", " i += 3\n", " elif pred_lines[i].startswith(\"P:\"):\n", " num_bitext += 1\n", " pred_str.append(pred_lines[i].removeprefix(\"P: \"))\n", " i += 1\n", " elif pred_lines[i].startswith(\"T:\"):\n", " label_str.append(pred_lines[i].removeprefix(\"T: \"))\n", " i += 1\n", " else:\n", " i += 1\n", " print(f\"Kept {len(pred_str)} out of {num_bitext} lines with length > 15\")\n", " long_metrics = eval_metrics(label_str, pred_str)\n", " print(f\"Long BLEU: {long_metrics['bleu']}\")\n", " print(f\"Long CHRF: {long_metrics['chrf']}\")\n", "\n", "print(\"BART: 16K\")\n", "calculate_metrics(\"test.pred.16K.man\")\n", "print(\"\")\n", " \n", "print(\"Base Phrase table: 80K\")\n", "calculate_metrics(\"test.pred.80K.man\")\n", "print(\"\")\n", "\n", "print(\"Base Phrase table with typo augmentation: 130K\")\n", "calculate_metrics(\"test.pred.130K.old.man\")\n", "print(\"\")\n", "\n", "print(\"Full Phrase table with typo augmentation: 130K\")\n", "calculate_metrics(\"test.pred.130K.new.12000.man\")\n", "print(\"\")\n", "\n", "print(\"Full Phrase table, bidirectional with typo augmentation: 175K\")\n", "calculate_metrics(\"test.pred.175K.12000.bidir.man\")\n", "print(\"\")\n", "\n", "print(\"Bing with typo augmentation: 300K\")\n", "calculate_metrics(\"test.pred.bing.11000.man\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[TYPOS] Base Phrase table: 80K\n", "Full BLEU: 25.67903890146884\n", "Full CHRF: 23.006809067678784\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 27.23221590137431\n", "Long CHRF: 25.098827658569146\n", "\n", "[TYPOS] Base Phrase table with typo augmentation: 130K\n", "Full BLEU: 28.852904211703883\n", "Full CHRF: 25.54057487338131\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 29.451457762359148\n", "Long CHRF: 26.957779099041645\n", "\n", "[TYPOS] Full Phrase table with typo augmentation: 130K\n", "Full BLEU: 27.67252498656469\n", "Full CHRF: 24.507617808716358\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 28.665473655102208\n", "Long CHRF: 26.19398178032962\n", "\n", "[TYPOS] Full Phrase table, bidirectional with typo augmentation: 175K\n", "Full BLEU: 27.968586584774183\n", "Full CHRF: 24.8085921158302\n", "Kept 832 out of 4237 lines with length > 15\n", "Long BLEU: 28.742461772965285\n", "Long CHRF: 26.311239247095287\n", "\n", "[TYPOS] Full Phrase table, mined bitext with typo augmentation: 170K\n", "Full BLEU: 25.70538675709905\n", "Full CHRF: 22.979575970979766\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 26.83479629562615\n", "Long CHRF: 24.798943718637688\n", "\n", "[TYPOS] Bing with typo augmentation: 100K\n", "Full BLEU: 31.782012081126922\n", "Full CHRF: 27.915281605247195\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 32.86274114601928\n", "Long CHRF: 30.336713749504685\n", "\n", "[TYPOS] Bing with typo augmentation: 300K\n", "Full BLEU: 37.445598287367865\n", "Full CHRF: 32.443127394648975\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 37.27638377311298\n", "Long CHRF: 33.950895249296394\n" ] } ], "source": [ "print(\"[TYPOS] Base Phrase table: 80K\")\n", "calculate_metrics(\"test.typos.pred.80K.7000.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Base Phrase table with typo augmentation: 130K\")\n", "calculate_metrics(\"test.typos.pred.130K.old.12000.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Full Phrase table with typo augmentation: 130K\")\n", "calculate_metrics(\"test.typos.pred.130K.new.12000.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Full Phrase table, bidirectional with typo augmentation: 175K\")\n", "calculate_metrics(\"test.typos.pred.175K.12000.bidir.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Full Phrase table, mined bitext with typo augmentation: 170K\")\n", "calculate_metrics(\"test.typos.pred.170K.mined.6000.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Bing with typo augmentation: 100K\")\n", "calculate_metrics(\"test.typos.pred.bing.8000.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Bing with typo augmentation: 300K\")\n", "calculate_metrics(\"test.typos.pred.bing.11000.man\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bing Translate:\n", "Full BLEU: 38.075374632013514\n", "Full CHRF: 33.38543089520182\n", "Kept 1026 out of 6502 lines with length > 15\n", "Long BLEU: 37.353351481695206\n", "Long CHRF: 34.13833877980428\n", "\n", "[TYPOS] Bing Translate:\n", "Full BLEU: 33.92564400445071\n", "Full CHRF: 29.856792221070954\n", "Kept 847 out of 4309 lines with length > 15\n", "Long BLEU: 35.012635782444036\n", "Long CHRF: 32.00001079226985\n", "\n" ] } ], "source": [ "# Commercial baseline\n", "\n", "def calculate_metrics(pred_file_path, tgt_file_path):\n", " # Normal eval\n", " with open(pred_file_path, \"r\") as pred_file, open(tgt_file_path, \"r\") as tgt_file:\n", " pred_str = pred_file.read().splitlines()\n", " label_str = tgt_file.read().splitlines()\n", " full_metrics = eval_metrics(label_str, pred_str)\n", " print(f\"Full BLEU: {full_metrics['bleu']}\")\n", " print(f\"Full CHRF: {full_metrics['chrf']}\")\n", "\n", " # Length > 15\n", " with open(pred_file_path, \"r\") as pred_file, open(tgt_file_path, \"r\") as tgt_file:\n", " pred_full_str = pred_file.read().splitlines()\n", " label_full_str = tgt_file.read().splitlines()\n", " pred_str = []\n", " label_str = []\n", " for pred_line, label_line in zip(pred_full_str, label_full_str):\n", " if len(label_line) > 15:\n", " pred_str.append(pred_line)\n", " label_str.append(label_line)\n", " print(f\"Kept {len(pred_str)} out of {len(pred_full_str)} lines with length > 15\")\n", " long_metrics = eval_metrics(label_str, pred_str)\n", " print(f\"Long BLEU: {long_metrics['bleu']}\")\n", " print(f\"Long CHRF: {long_metrics['chrf']}\")\n", "\n", "print(\"Bing Translate:\")\n", "calculate_metrics(\"test.pred.bing.man\", \"para/test/test.man\")\n", "print(\"\")\n", "\n", "print(\"[TYPOS] Bing Translate:\")\n", 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