{"cells":[{"cell_type":"code","execution_count":209,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":210,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":211,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":211,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":212,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["01-ai/Yi-1.5-9B-Chat None False datasets/mac/mac.tsv results/mac-results.csv False 300\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path, use_english_datasets, max_new_tokens)"]},{"cell_type":"code","execution_count":213,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n"]},{"name":"stderr","output_type":"stream","text":["python(9709) MallocStackLogging: can't turn off malloc stack logging because it was not enabled.\n","python(9710) MallocStackLogging: can't turn off malloc stack logging because it was not enabled.\n"]},{"name":"stdout","output_type":"stream","text":["Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 11.1 ms, sys: 23 ms, total: 34.1 ms\n","Wall time: 2.03 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":214,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":215,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1133 entries, 0 to 1132\n","Columns: 110 entries, chinese to internlm/internlm2_5-7b-chat/rpp-1.24\n","dtypes: object(110)\n","memory usage: 973.8+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":216,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.00',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.02',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.04',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.06',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.08',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.10',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.12',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.14',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.16',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.18',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.20',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.22',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.24',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.26',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.28',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.30',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.30',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.00',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.02',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.04',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.06',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.08',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.10',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.12',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.14',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.16',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.18',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.20',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.22',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":216,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.remove(\"01-ai/Yi-1.5-34B-Chat/rpp-1.00\")\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":217,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["01-ai/Yi-1.5-9B-Chat/rpp-1.00: {'meteor': 0.3463725436435439, 'bleu_scores': {'bleu': 0.09312113035602035, 'precisions': [0.37803102247546694, 0.1276225498243425, 0.05633754814082683, 0.027665603967410555], 'brevity_penalty': 1.0, 'length_ratio': 1.0463729711825107, 'translation_length': 31590, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3870139699578016, 'rouge2': 0.1488247506004683, 'rougeL': 0.33287597095291194, 'rougeLsum': 0.33363484077183997}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.02: {'meteor': 0.3471185374158656, 'bleu_scores': {'bleu': 0.09126513887574451, 'precisions': [0.37119079293382423, 0.12507213850593138, 0.055267358339984037, 0.027039160162994683], 'brevity_penalty': 1.0, 'length_ratio': 1.0706525339516395, 'translation_length': 32323, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.387830080294432, 'rouge2': 0.14937986353938124, 'rougeL': 0.3325894211716421, 'rougeLsum': 0.33382464511623333}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.04: {'meteor': 0.3471882673119874, 'bleu_scores': {'bleu': 0.09019886552461354, 'precisions': [0.3666473689021603, 0.12279871236508237, 0.054601367487813655, 0.026925166372402554], 'brevity_penalty': 1.0, 'length_ratio': 1.0870818151705863, 'translation_length': 32819, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38716810370843213, 'rouge2': 0.14788629925943475, 'rougeL': 0.33194600115482237, 'rougeLsum': 0.33319395641802385}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.06: {'meteor': 0.3475947948648639, 'bleu_scores': {'bleu': 0.09004996084071014, 'precisions': [0.36712303648921213, 0.12323910221912691, 0.05448160425350356, 0.02667620605069501], 'brevity_penalty': 1.0, 'length_ratio': 1.0838688307386553, 'translation_length': 32722, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38647707126964187, 'rouge2': 0.14858152809156483, 'rougeL': 0.33128175611085137, 'rougeLsum': 0.33251496687144244}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.08: {'meteor': 0.34751102711658816, 'bleu_scores': {'bleu': 0.09004837948083254, 'precisions': [0.3674401495877142, 0.12270562083201016, 0.05426765015806112, 0.026872713597046054], 'brevity_penalty': 1.0, 'length_ratio': 1.0805895992050347, 'translation_length': 32623, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38788324655708895, 'rouge2': 0.1484143855570148, 'rougeL': 0.33142704269248113, 'rougeLsum': 0.3329090457320951}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.10: {'meteor': 0.3483026982644252, 'bleu_scores': {'bleu': 0.08832819752923171, 'precisions': [0.36867336590762423, 0.12191161487519103, 0.052867945712115706, 0.025616405473063337], 'brevity_penalty': 1.0, 'length_ratio': 1.0778734680357733, 'translation_length': 32541, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38805870113692587, 'rouge2': 0.14772052151524306, 'rougeL': 0.3310725986559182, 'rougeLsum': 0.3322161297004944}, 'accuracy': 0.0, 'correct_ids': []}\n","01-ai/Yi-1.5-9B-Chat/rpp-1.12: {'meteor': 0.34415914233475586, 'bleu_scores': {'bleu': 0.08286056438796492, 'precisions': [0.3471529630059144, 0.11337270301867562, 0.049294245385450595, 0.024297743780934626], 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0.37395352240115537, 'bleu_scores': {'bleu': 0.10626828666161364, 'precisions': [0.42186919046814264, 0.14828483059187403, 0.06760614934114202, 0.032724912173514586], 'brevity_penalty': 0.9797581219304219, 'length_ratio': 0.9799602517389864, 'translation_length': 29585, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41965973391778166, 'rouge2': 0.17138205536099307, 'rougeL': 0.36521120193157575, 'rougeLsum': 0.3652954751913341}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.20: {'meteor': 0.3723192971207163, 'bleu_scores': {'bleu': 0.10393463024709437, 'precisions': [0.42008869629980705, 0.14591987608251777, 0.06596025518809122, 0.03152015913090047], 'brevity_penalty': 0.9782024162775965, 'length_ratio': 0.9784365684001325, 'translation_length': 29539, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4186753905197945, 'rouge2': 0.16941584464464193, 'rougeL': 0.363364555923275, 'rougeLsum': 0.36350586414162567}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.22: {'meteor': 0.3705806513041972, 'bleu_scores': {'bleu': 0.10261551646100059, 'precisions': [0.4183030877573131, 0.14442136394042882, 0.06479410362656302, 0.030949921573128276], 'brevity_penalty': 0.9781008748911221, 'length_ratio': 0.9783371977475985, 'translation_length': 29536, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41769608740052666, 'rouge2': 0.1678747258910918, 'rougeL': 0.3622619214203682, 'rougeLsum': 0.3623007318265357}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.24: {'meteor': 0.36850054764539125, 'bleu_scores': {'bleu': 0.10177259617775142, 'precisions': [0.4137083192994274, 0.14283713275203977, 0.06406563354603464, 0.030312250408854068], 'brevity_penalty': 0.9833003245834433, 'length_ratio': 0.9834382245776747, 'translation_length': 29690, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41599269937909433, 'rouge2': 0.166951474940792, 'rougeL': 0.3607696198140324, 'rougeLsum': 0.3609431614834704}, 'accuracy': 0.00088261253309797, 'correct_ids': [531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.26: {'meteor': 0.365757498062885, 'bleu_scores': {'bleu': 0.09925621878391376, 'precisions': [0.4130685675492193, 0.14050199456349066, 0.06258503401360545, 0.029505429152438322], 'brevity_penalty': 0.9755251263477148, 'length_ratio': 0.975819807883405, 'translation_length': 29460, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4140788408725167, 'rouge2': 0.16395262819771916, 'rougeL': 0.3579645404381391, 'rougeLsum': 0.3579833609740545}, 'accuracy': 0.00088261253309797, 'correct_ids': [531]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00: {'meteor': 0.3579680086793429, 'bleu_scores': {'bleu': 0.10207096308148353, 'precisions': [0.4050919842312746, 0.13836284846623673, 0.06271739902037339, 0.030877893646919608], 'brevity_penalty': 1.0, 'length_ratio': 1.0082808877111626, 'translation_length': 30440, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39946884115694326, 'rouge2': 0.15450624863552764, 'rougeL': 0.3460132814937531, 'rougeLsum': 0.34654876040667026}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02: {'meteor': 0.3572226770743513, 'bleu_scores': {'bleu': 0.10061303169730976, 'precisions': [0.40227130994190435, 0.13629235699188655, 0.0616999397184497, 0.030292955040821603], 'brevity_penalty': 1.0, 'length_ratio': 1.0091752235839682, 'translation_length': 30467, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3980508614351076, 'rouge2': 0.1526528429743093, 'rougeL': 0.3450507994469454, 'rougeLsum': 0.3453050258410778}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04: {'meteor': 0.35670586983276636, 'bleu_scores': {'bleu': 0.10074138007196803, 'precisions': [0.40298261785620226, 0.13664808672160858, 0.06154174522428942, 0.03039288361749444], 'brevity_penalty': 1.0, 'length_ratio': 1.006160980457105, 'translation_length': 30376, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39843773529055504, 'rouge2': 0.15316920521842195, 'rougeL': 0.3450245802338977, 'rougeLsum': 0.3453769207760845}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06: {'meteor': 0.35549318326656437, 'bleu_scores': {'bleu': 0.0998891248706679, 'precisions': [0.40340965407869955, 0.13568339397267798, 0.060638525819584316, 0.02999516207063377], 'brevity_penalty': 1.0, 'length_ratio': 1.002550513415038, 'translation_length': 30267, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39700820058157305, 'rouge2': 0.1517563058419956, 'rougeL': 0.3434279258982189, 'rougeLsum': 0.34362751592688234}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08: {'meteor': 0.3549933805160392, 'bleu_scores': {'bleu': 0.09858894278315135, 'precisions': [0.40283608237199614, 0.13413461538461538, 0.059813484832243545, 0.02923105566933532], 'brevity_penalty': 1.0, 'length_ratio': 1.002086783703213, 'translation_length': 30253, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3970286097371607, 'rouge2': 0.15140785635415274, 'rougeL': 0.34312451283209056, 'rougeLsum': 0.3433639286482863}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10: {'meteor': 0.3534792705039357, 'bleu_scores': {'bleu': 0.09604337437044752, 'precisions': [0.3991737163092662, 0.13114251660139623, 0.05780674412014735, 0.02811808118081181], 'brevity_penalty': 1.0, 'length_ratio': 1.0102020536601524, 'translation_length': 30498, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3959019445169253, 'rouge2': 0.14990100131704065, 'rougeL': 0.3418333251931206, 'rougeLsum': 0.34204403664135463}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12: {'meteor': 0.35134989369086755, 'bleu_scores': {'bleu': 0.09466593964355864, 'precisions': [0.39743042092465414, 0.12914675767918088, 0.05662654879823907, 0.02763187097728786], 'brevity_penalty': 1.0, 'length_ratio': 1.00804902285525, 'translation_length': 30433, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39500962722629557, 'rouge2': 0.14860484087031106, 'rougeL': 0.3406575067978035, 'rougeLsum': 0.34107753392813356}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14: {'meteor': 0.3523467471502627, 'bleu_scores': {'bleu': 0.09455136235619709, 'precisions': [0.3977223964350553, 0.1297921953226802, 0.05665560669306789, 0.027327483640690067], 'brevity_penalty': 1.0, 'length_ratio': 1.0034779728386882, 'translation_length': 30295, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39433403791206834, 'rouge2': 0.14902303389483895, 'rougeL': 0.3405868200118059, 'rougeLsum': 0.34083130713118903}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16: {'meteor': 0.35026382260485167, 'bleu_scores': {'bleu': 0.09291738095604976, 'precisions': [0.39490550534100244, 0.12826027584323366, 0.05539772727272727, 0.026565043658428298], 'brevity_penalty': 1.0, 'length_ratio': 1.007784034448493, 'translation_length': 30425, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3924325460715663, 'rouge2': 0.14713680030512016, 'rougeL': 0.3381480886207706, 'rougeLsum': 0.3384999431539848}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18: {'meteor': 0.3489231946755186, 'bleu_scores': {'bleu': 0.09267866809703615, 'precisions': [0.39275271855185784, 0.1265269910598512, 0.05529921203946901, 0.026847126691812735], 'brevity_penalty': 1.0, 'length_ratio': 1.008247764160318, 'translation_length': 30439, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39237402092494467, 'rouge2': 0.1464648085024951, 'rougeL': 0.33749580508212734, 'rougeLsum': 0.3377716538383176}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20: {'meteor': 0.3481931091877492, 'bleu_scores': {'bleu': 0.09121903225057944, 'precisions': [0.3936789209203914, 0.12509015971148893, 0.05385412571918665, 0.026107035119734834], 'brevity_penalty': 1.0, 'length_ratio': 1.0019211659489897, 'translation_length': 30248, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3918332923129943, 'rouge2': 0.14527102745176168, 'rougeL': 0.3363167460865901, 'rougeLsum': 0.3364591522497503}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22: {'meteor': 0.34604714296451533, 'bleu_scores': {'bleu': 0.08945165053230478, 'precisions': [0.3885469189967913, 0.12268353225203169, 0.052763730240124485, 0.025455885061705655], 'brevity_penalty': 1.0, 'length_ratio': 1.011659489897317, 'translation_length': 30542, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.390242603484803, 'rouge2': 0.14416186409541937, 'rougeL': 0.3352830183155636, 'rougeLsum': 0.3356373582520039}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24: {'meteor': 0.3441084154272239, 'bleu_scores': {'bleu': 0.0880200303756021, 'precisions': [0.38647311334665924, 0.12112033759869317, 0.05213790174146963, 0.02459439528023599], 'brevity_penalty': 1.0, 'length_ratio': 1.0108314011262007, 'translation_length': 30517, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3884851286721268, 'rouge2': 0.14279769133731374, 'rougeL': 0.3327376500632496, 'rougeLsum': 0.33315920142771044}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26: {'meteor': 0.3434534163683513, 'bleu_scores': {'bleu': 0.08571979267389605, 'precisions': [0.3820319880126388, 0.11814246093485761, 0.05071393402264894, 0.023588015529997803], 'brevity_penalty': 1.0, 'length_ratio': 1.016859887379927, 'translation_length': 30699, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3869321383577401, 'rouge2': 0.14174733998072325, 'rougeL': 0.33067392953084385, 'rougeLsum': 0.3311395804213585}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28: {'meteor': 0.34008394315191964, 'bleu_scores': {'bleu': 0.08346595677194628, 'precisions': [0.3769493732703891, 0.11567845311337976, 0.049279437609841825, 0.022585840837543013], 'brevity_penalty': 1.0, 'length_ratio': 1.0173898641934416, 'translation_length': 30715, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38455514917396016, 'rouge2': 0.13989244725746022, 'rougeL': 0.3280102626306619, 'rougeLsum': 0.32830974480773334}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30: {'meteor': 0.3385373237572206, 'bleu_scores': {'bleu': 0.08244181010811574, 'precisions': [0.3770232925384919, 0.11512831903769265, 0.04870072162383136, 0.021852661209674433], 'brevity_penalty': 1.0, 'length_ratio': 1.006823451473998, 'translation_length': 30396, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38289452420576187, 'rouge2': 0.13898174896063814, 'rougeL': 0.32684753756927853, 'rougeLsum': 0.3273410937194262}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00: {'meteor': 0.3256642047768536, 'bleu_scores': {'bleu': 0.08331314362646546, 'precisions': [0.37692207876467915, 0.11804128919273903, 0.04877450980392157, 0.022201159272356094], 'brevity_penalty': 1.0, 'length_ratio': 1.0210665783371977, 'translation_length': 30826, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36840713201876146, 'rouge2': 0.13299426456171795, 'rougeL': 0.3161580747851038, 'rougeLsum': 0.3167048142599916}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:257: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  count_entries_with_max_tokens(df[new_col], max_output_tokens)\n","/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02: {'meteor': 0.3261638331201866, 'bleu_scores': {'bleu': 0.08437219278343962, 'precisions': [0.37692532183274424, 0.1178213155591463, 0.04962727050012249, 0.02299311299785009], 'brevity_penalty': 1.0, 'length_ratio': 1.0214971844981782, 'translation_length': 30839, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3683327223208172, 'rouge2': 0.13298879061116414, 'rougeL': 0.3160165886106982, 'rougeLsum': 0.3166083249633809}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04: {'meteor': 0.3261267542205407, 'bleu_scores': {'bleu': 0.0841026780937562, 'precisions': [0.37486681088760454, 0.11693142972049064, 0.04964291935202926, 0.02299184043517679], 'brevity_penalty': 1.0, 'length_ratio': 1.0258694932096721, 'translation_length': 30971, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36784115591407124, 'rouge2': 0.13273405519793757, 'rougeL': 0.31586790820617083, 'rougeLsum': 0.31659574673209057}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06: {'meteor': 0.32610191030444663, 'bleu_scores': {'bleu': 0.08440911364941035, 'precisions': [0.37549304881991596, 0.11705876430513139, 0.04960926597823053, 0.02328030798285756], 'brevity_penalty': 1.0, 'length_ratio': 1.0245114276250413, 'translation_length': 30930, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36752925022525673, 'rouge2': 0.13217466088334368, 'rougeL': 0.3156161826682502, 'rougeLsum': 0.31628238804685416}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08: {'meteor': 0.32519072627069395, 'bleu_scores': {'bleu': 0.08573531403311445, 'precisions': [0.3768451236599433, 0.11825010150223304, 0.05052246420152693, 0.023998827538196606], 'brevity_penalty': 1.0, 'length_ratio': 1.0165286518714807, 'translation_length': 30689, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3677681318911758, 'rouge2': 0.1329334511082953, 'rougeL': 0.31555219872015555, 'rougeLsum': 0.3162169797197245}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10: {'meteor': 0.32510929376904546, 'bleu_scores': {'bleu': 0.08572184129459336, 'precisions': [0.3766598153404457, 0.11731824649366489, 0.05030826140567201, 0.024289121262153733], 'brevity_penalty': 1.0, 'length_ratio': 1.015269956939384, 'translation_length': 30651, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3669925918468957, 'rouge2': 0.1317690468418684, 'rougeL': 0.3143439978950341, 'rougeLsum': 0.31499486147109523}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12: {'meteor': 0.325321692973156, 'bleu_scores': {'bleu': 0.08501006133800607, 'precisions': [0.3769911504424779, 0.11597508254757123, 0.0496742671009772, 0.024046617983329646], 'brevity_penalty': 1.0, 'length_ratio': 1.0105995362702882, 'translation_length': 30510, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3670918439535572, 'rouge2': 0.1306394142574278, 'rougeL': 0.3136378009708979, 'rougeLsum': 0.31448454091818295}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14: {'meteor': 0.3224620858016468, 'bleu_scores': {'bleu': 0.08389328832417228, 'precisions': [0.3779330345373056, 0.11529903118688166, 0.048935109338271957, 0.02322992429864925], 'brevity_penalty': 1.0, 'length_ratio': 1.0051010268300762, 'translation_length': 30344, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3660029478823349, 'rouge2': 0.12962198881927703, 'rougeL': 0.3130154415556936, 'rougeLsum': 0.3138353845845071}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16: {'meteor': 0.32354623636120206, 'bleu_scores': {'bleu': 0.08389983318570625, 'precisions': [0.3772855017358241, 0.11575982412750756, 0.04921372408863474, 0.02305314513425943], 'brevity_penalty': 1.0, 'length_ratio': 1.0018217952964559, 'translation_length': 30245, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.365798410378833, 'rouge2': 0.13022724788126894, 'rougeL': 0.31361563891120947, 'rougeLsum': 0.31418770957030584}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18: {'meteor': 0.3227464993995023, 'bleu_scores': {'bleu': 0.08237511984991769, 'precisions': [0.37662723848542917, 0.11529880204579, 0.04821256383700582, 0.02199315272402501], 'brevity_penalty': 1.0, 'length_ratio': 1.0025173898641935, 'translation_length': 30266, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3649625928411872, 'rouge2': 0.1297823979809622, 'rougeL': 0.31237472571694164, 'rougeLsum': 0.3130341342775994}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20: {'meteor': 0.3213479416591043, 'bleu_scores': {'bleu': 0.08021470447158471, 'precisions': [0.3734951746094916, 0.11340454858718126, 0.046686746987951805, 0.021039650211143915], 'brevity_penalty': 0.9987736772994305, 'length_ratio': 0.9987744286187479, 'translation_length': 30153, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3633099524507924, 'rouge2': 0.1279994669647978, 'rougeL': 0.31081287893463483, 'rougeLsum': 0.311576974320659}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22: {'meteor': 0.31939727082775615, 'bleu_scores': {'bleu': 0.08027275774782588, 'precisions': [0.37060882197569994, 0.11191905333561997, 0.04649751989437248, 0.021528965568528298], 'brevity_penalty': 1.0, 'length_ratio': 1.0032461079827757, 'translation_length': 30288, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3609380986777075, 'rouge2': 0.12666125324918132, 'rougeL': 0.3089835285121734, 'rougeLsum': 0.30956638915014134}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24: {'meteor': 0.3188662188138966, 'bleu_scores': {'bleu': 0.07877965659256216, 'precisions': [0.3695673695673696, 0.11004456633527597, 0.045509665454026675, 0.020810881117841615], 'brevity_penalty': 1.0, 'length_ratio': 1.0037429612454456, 'translation_length': 30303, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35966629764151414, 'rouge2': 0.1255987660701956, 'rougeL': 0.30728620231759696, 'rougeLsum': 0.3077173322184259}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26: {'meteor': 0.31805084189335, 'bleu_scores': {'bleu': 0.07777595035895293, 'precisions': [0.36718209093007154, 0.10867182683745462, 0.04475165680895033, 0.020491498997698417], 'brevity_penalty': 1.0, 'length_ratio': 1.0046704206690957, 'translation_length': 30331, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3586578737816579, 'rouge2': 0.12488061546195132, 'rougeL': 0.30667694159970027, 'rougeLsum': 0.30730677797657274}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28: {'meteor': 0.31564132115319793, 'bleu_scores': {'bleu': 0.07471248687074669, 'precisions': [0.3653415084388186, 0.1064959079546622, 0.0426418723949984, 0.018780388226997735], 'brevity_penalty': 1.0, 'length_ratio': 1.004836038423319, 'translation_length': 30336, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3575069575446436, 'rouge2': 0.12384165440953143, 'rougeL': 0.3046949012325021, 'rougeLsum': 0.3054690222171944}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30: {'meteor': 0.31448483374273595, 'bleu_scores': {'bleu': 0.07484673889486904, 'precisions': [0.36305669679539854, 0.10600163867267513, 0.04272017045454545, 0.01908848771825984], 'brevity_penalty': 1.0, 'length_ratio': 1.007784034448493, 'translation_length': 30425, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35601587422350284, 'rouge2': 0.1229164691279465, 'rougeL': 0.3035257437090866, 'rougeLsum': 0.30386441333286196}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:262: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  metrics_df[\"rouge_l\"] = rouge_l\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>rpp</th>\n","      <th>meteor</th>\n","      <th>bleu_1</th>\n","      <th>rouge_l</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>rap</th>\n","      <th>num_max_output_tokens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.00</td>\n","      <td>0.346373</td>\n","      <td>0.093121</td>\n","      <td>0.332876</td>\n","      <td>0.0</td>\n","      <td>0.351280</td>\n","      <td>0.351280</td>\n","      <td>0.341256</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.02</td>\n","      <td>0.347119</td>\n","      <td>0.091265</td>\n","      <td>0.332589</td>\n","      <td>0.0</td>\n","      <td>0.264784</td>\n","      <td>0.264784</td>\n","      <td>0.343223</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.04</td>\n","      <td>0.347188</td>\n","      <td>0.090199</td>\n","      <td>0.331946</td>\n","      <td>0.0</td>\n","      <td>0.377758</td>\n","      <td>0.377758</td>\n","      <td>0.341686</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.06</td>\n","      <td>0.347595</td>\n","      <td>0.090050</td>\n","      <td>0.331282</td>\n","      <td>0.0</td>\n","      <td>0.468667</td>\n","      <td>0.468667</td>\n","      <td>0.340815</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.08</td>\n","      <td>0.347511</td>\n","      <td>0.090048</td>\n","      <td>0.331427</td>\n","      <td>0.0</td>\n","      <td>0.311562</td>\n","      <td>0.311562</td>\n","      <td>0.342942</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.319397</td>\n","      <td>0.080273</td>\n","      <td>0.308984</td>\n","      <td>0.0</td>\n","      <td>0.100618</td>\n","      <td>0.100618</td>\n","      <td>0.318015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.318866</td>\n","      <td>0.078780</td>\n","      <td>0.307286</td>\n","      <td>0.0</td>\n","      <td>0.082083</td>\n","      <td>0.082083</td>\n","      <td>0.317738</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.318051</td>\n","      <td>0.077776</td>\n","      <td>0.306677</td>\n","      <td>0.0</td>\n","      <td>0.073257</td>\n","      <td>0.073257</td>\n","      <td>0.317046</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>105</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.315641</td>\n","      <td>0.074712</td>\n","      <td>0.304695</td>\n","      <td>0.0</td>\n","      <td>0.057370</td>\n","      <td>0.057370</td>\n","      <td>0.314859</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.314485</td>\n","      <td>0.074847</td>\n","      <td>0.303526</td>\n","      <td>0.0</td>\n","      <td>0.067961</td>\n","      <td>0.067961</td>\n","      <td>0.313562</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>107 rows × 10 columns</p>\n","</div>"],"text/plain":["                                         model   rpp    meteor    bleu_1  \\\n","0                         01-ai/Yi-1.5-9B-Chat  1.00  0.346373  0.093121   \n","1                         01-ai/Yi-1.5-9B-Chat  1.02  0.347119  0.091265   \n","2                         01-ai/Yi-1.5-9B-Chat  1.04  0.347188  0.090199   \n","3                         01-ai/Yi-1.5-9B-Chat  1.06  0.347595  0.090050   \n","4                         01-ai/Yi-1.5-9B-Chat  1.08  0.347511  0.090048   \n","..                                         ...   ...       ...       ...   \n","102  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.22  0.319397  0.080273   \n","103  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.24  0.318866  0.078780   \n","104  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.26  0.318051  0.077776   \n","105  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.28  0.315641  0.074712   \n","106  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.30  0.314485  0.074847   \n","\n","      rouge_l  ews_score  repetition_score  total_repetitions       rap  \\\n","0    0.332876        0.0          0.351280           0.351280  0.341256   \n","1    0.332589        0.0          0.264784           0.264784  0.343223   \n","2    0.331946        0.0          0.377758           0.377758  0.341686   \n","3    0.331282        0.0          0.468667           0.468667  0.340815   \n","4    0.331427        0.0          0.311562           0.311562  0.342942   \n","..        ...        ...               ...                ...       ...   \n","102  0.308984        0.0          0.100618           0.100618  0.318015   \n","103  0.307286        0.0          0.082083           0.082083  0.317738   \n","104  0.306677        0.0          0.073257           0.073257  0.317046   \n","105  0.304695        0.0          0.057370           0.057370  0.314859   \n","106  0.303526        0.0          0.067961           0.067961  0.313562   \n","\n","     num_max_output_tokens  \n","0                        2  \n","1                        4  \n","2                        8  \n","3                        9  \n","4                        4  \n","..                     ...  \n","102                      0  \n","103                      0  \n","104                      0  \n","105                      0  \n","106                      0  \n","\n","[107 rows x 10 columns]"]},"execution_count":217,"metadata":{},"output_type":"execute_result"}],"source":["df = df[columns]\n","metrics_df = get_metrics(df, max_output_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":218,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading /Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py\n"]},{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package wordnet to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package wordnet is already up-to-date!\n","[nltk_data] Downloading package punkt to /Users/inflaton/nltk_data...\n","[nltk_data]   Package punkt is already up-to-date!\n","[nltk_data] Downloading package omw-1.4 to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package omw-1.4 is already up-to-date!\n"]},{"data":{"text/plain":["array(['01-ai/Yi-1.5-9B-Chat', 'Qwen/Qwen2-72B-Instruct',\n","       'Qwen/Qwen2-7B-Instruct', 'internlm/internlm2_5-7b-chat',\n","       'shenzhi-wang/Llama3.1-70B-Chinese-Chat',\n","       'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n","       'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":218,"metadata":{},"output_type":"execute_result"}],"source":["models = metrics_df[\"model\"].unique()\n","models"]},{"cell_type":"code","execution_count":219,"metadata":{},"outputs":[],"source":["# list of markers for plotting\n","markers = [\"o\", \"x\", \"^\", \"s\", \"d\", \"P\", \"X\", \"*\", \"v\", \">\", \"<\", \"p\", \"h\", \"H\", \"+\", \"|\", \"_\"]\n","markers = {model: marker for model, marker in zip(models, markers)}"]},{"cell_type":"code","execution_count":220,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"meteor\"],\n","        label=model + \" (METEOR)\",\n","        marker=markers[model],\n","    )\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"rap\"],\n","        label=model + \" (RAP-METEOR)\",\n","        linestyle=\"--\",\n","        marker=markers[model],\n","    )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.85))\n","plt.show()"]},{"cell_type":"code","execution_count":221,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(\n","    which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\"\n",")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"total_repetitions\"],\n","        label=model,\n","        marker=markers[model],\n","    )\n","\n","# ax.set_ylim(0, 1)\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"num_max_output_tokens\"], label=model, marker=markers[model])\n","\n","# ax.set_ylim(0, 1)\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Number of Answers with Max Output Tokens\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":223,"metadata":{},"outputs":[],"source":["def detect_repetitions_for_model_outputs(df, col, threshold=100):\n","    df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n","        detect_scores\n","    )\n","    return df.query(f\"total_repetitions > {threshold}\")"]},{"cell_type":"code","execution_count":224,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.14</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… No…\"</td>\n","      <td>...</td>\n","      <td>142</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>759</th>\n","      <td>我是个什么东西儿!</td>\n","      <td>What sort of creature do you take me for?</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>...</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>15</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>36</td>\n","      <td>36</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 229 columns</p>\n","</div>"],"text/plain":["                chinese                                    english  \\\n","193  “有…… 没有…… 有…… 没有……     'Yes . . . no . . . yes . . . no . . .   \n","759           我是个什么东西儿!  What sort of creature do you take me for?   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.00 01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","193       \"Yes…… no…… yes…… no……\"       \"Yes…… no…… yes…… no……\"   \n","759      What kind of thing am I!      What kind of thing am I!   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.04 01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","193       \"Yes…… no…… yes…… no……\"       \"Yes…… no…… yes…… no……\"   \n","759      What kind of thing am I?      What kind of thing am I?   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.08 01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","193           \"Yes… no… Yes… no…\"           \"Yes… no… Yes… no…\"   \n","759      What kind of thing am I?      What kind of thing am I?   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.12 01-ai/Yi-1.5-9B-Chat/rpp-1.14  ...  \\\n","193           \"Yes… no… Yes… no…\"           \"Yes… no… Yes… No…\"  ...   \n","759      What kind of thing am I?      What kind of thing am I?  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","193                                                142                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","193                                                  9                 \n","759                                                 15                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","193                                                  9                 \n","759                                                 11                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","193                                                  9                 \n","759                                                 11                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","193                                                  9                 \n","759                                                 36                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","193                                                  9                \n","759                                                 36                \n","\n","[2 rows x 229 columns]"]},"execution_count":224,"metadata":{},"output_type":"execute_result"}],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","rows = detect_repetitions_for_model_outputs(df, col)\n","rows"]},{"cell_type":"code","execution_count":225,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n","'Yes . . . no . . . yes . . . no . . .\n","Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(159, 943), match=\":\\n\\nYes, I can help you with that! Here's the tr>\n","Group 1 found at 159-551: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 784, 784)\n"]},{"data":{"text/plain":["(0, 784, 784)"]},"execution_count":225,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[0]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":226,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["我是个什么东西儿!\n","What sort of creature do you take me for?\n","I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"What am I?\"\n","\n","The answer is: \"I am a Chinese-English translator.\"\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(0, 148), match='I am a Chinese-English translator. Here is the tr>\n","Group 1 found at 0-74: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","Group 2 found at 74-148: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","Group 3 found at 74-148: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","(0, 148, 148)\n"]},{"data":{"text/plain":["(0, 148, 148)"]},"execution_count":226,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[1]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":227,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.14</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 229 columns</p>\n","</div>"],"text/plain":["                                               chinese  \\\n","327  短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","\n","                                               english  \\\n","327  short-long-long-long-long, short-long-long-lon...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","327  This is a sequence of words and numbers: \"长长长长...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.12  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.14  ...  \\\n","327  This is a sequence of words: \"short long long ...  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","327                                                 83                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","327                                                 61                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","327                                                 81                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","327                                                 65                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","327                                                 64                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","327                                                120                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","327                                                202                \n","\n","[1 rows x 229 columns]"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["col = \"Qwen/Qwen2-7B-Instruct/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=50)\n","rows"]},{"cell_type":"code","execution_count":228,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","The sequence you provided seems to be a series of patterns or codes, possibly related to a specific context or system. Here is the translation of the sequence into English:\n","\n","\"Short long long long, short long long long, short short short short, long long short short, long long short long, short short long long, short short short long, long long short long, long long short short.\"\n","\n","This sequence, \"1108:21:37,\" appears to be a timestamp or a code. However, without additional context, it's unclear what this sequence represents or how it relates to the preceding text.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(176, 220), match='hort long long long, short long long long, s'>\n","Group 1 found at 176-198: `hort long long long, s`\n","Group 2 found at 198-220: `hort long long long, s`\n","Group 3 found at 198-220: `hort long long long, s`\n","<re.Match object; span=(220, 238), match='hort short short s'>\n","Group 1 found at 220-226: `hort s`\n","Group 2 found at 232-238: `hort s`\n","Group 3 found at 232-238: `hort s`\n","<re.Match object; span=(243, 254), match=' long long '>\n","Group 1 found at 243-248: ` long`\n","Group 2 found at 248-254: ` long `\n","Group 3 found at 248-253: ` long`\n","<re.Match object; span=(254, 265), match='short short'>\n","Group 1 found at 254-259: `short`\n","Group 2 found at 260-265: `short`\n","Group 3 found at 260-265: `short`\n","<re.Match object; span=(266, 277), match=' long long '>\n","Group 1 found at 266-271: ` long`\n","Group 2 found at 271-277: ` long `\n","Group 3 found at 271-276: ` long`\n","<re.Match object; span=(288, 301), match=' short short '>\n","Group 1 found at 288-294: ` short`\n","Group 2 found at 294-301: ` short `\n","Group 3 found at 294-300: ` short`\n","<re.Match object; span=(311, 324), match=' short short '>\n","Group 1 found at 311-317: ` short`\n","Group 2 found at 317-324: ` short `\n","Group 3 found at 317-323: ` short`\n","<re.Match object; span=(324, 368), match='short long, long long short long, long long '>\n","Group 1 found at 324-346: `short long, long long `\n","Group 2 found at 346-368: `short long, long long `\n","Group 3 found at 346-368: `short long, long long `\n","<re.Match object; span=(368, 379), match='short short'>\n","Group 1 found at 368-373: `short`\n","Group 2 found at 374-379: `short`\n","Group 3 found at 374-379: `short`\n","(0, 176, 176)\n"]}],"source":["for i, row in rows.iterrows():\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":230,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":230,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":231,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.14</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… No…\"</td>\n","      <td>...</td>\n","      <td>142</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>1045</th>\n","      <td>高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。</td>\n","      <td>Erect stalks of sorghum formed dense barriers ...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>...</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>32</td>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>32</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 229 columns</p>\n","</div>"],"text/plain":["                                                chinese  \\\n","193                                  “有…… 没有…… 有…… 没有……   \n","327   短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","1045       高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。   \n","\n","                                                english  \\\n","193              'Yes . . . no . . . yes . . . no . . .   \n","327   short-long-long-long-long, short-long-long-lon...   \n","1045  Erect stalks of sorghum formed dense barriers ...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words and numbers: \"长长长长...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","193                                 \"Yes… no… Yes… no…\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","193                                 \"Yes… no… Yes… no…\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.12  \\\n","193                                 \"Yes… no… Yes… no…\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.14  ...  \\\n","193                                 \"Yes… no… Yes… No…\"  ...   \n","327   This is a sequence of words: \"short long long ...  ...   \n","1045  The tall stalks of sorghum form a dense fence,...  ...   \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","193                                                 142                 \n","327                                                  83                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","193                                                   9                 \n","327                                                  61                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","193                                                   9                 \n","327                                                  81                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 32                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","193                                                   9                 \n","327                                                  65                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","193                                                   9                 \n","327                                                  64                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","193                                                   9                 \n","327                                                 120                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","193                                                   9                \n","327                                                 202                \n","1045                                                 32                \n","\n","[3 rows x 229 columns]"]},"execution_count":231,"metadata":{},"output_type":"execute_result"}],"source":["col = \"internlm/internlm2_5-7b-chat/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=30)\n","rows"]},{"cell_type":"code","execution_count":232,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n","================================================================================\n","'Yes . . . no . . . yes . . . no . . .\n","================================================================================\n","\"Have... Don't have... Have... Don't have...\"\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(1, 44), match=\"Have... Don't have... Have... Don't have...\">\n","Group 1 found at 1-22: `Have... Don't have...`\n","Group 2 found at 23-44: `Have... Don't have...`\n","Group 3 found at 23-44: `Have... Don't have...`\n","(0, 43, 43)\n","短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","Short long long long, short long long long, short short short, long long short, long long short long, short short long long, short short short, long long short long, short short short long, long long short short, this is 1108:21:37.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(1, 45), match='hort long long long, short long long long, s'>\n","Group 1 found at 1-23: `hort long long long, s`\n","Group 2 found at 23-45: `hort long long long, s`\n","Group 3 found at 23-45: `hort long long long, s`\n","<re.Match object; span=(45, 57), match='hort short s'>\n","Group 1 found at 45-51: `hort s`\n","Group 2 found at 51-57: `hort s`\n","Group 3 found at 51-57: `hort s`\n","<re.Match object; span=(57, 91), match='hort, long long short, long long s'>\n","Group 1 found at 57-74: `hort, long long s`\n","Group 2 found at 74-91: `hort, long long s`\n","Group 3 found at 74-91: `hort, long long s`\n","<re.Match object; span=(101, 114), match=' short short '>\n","Group 1 found at 101-107: ` short`\n","Group 2 found at 107-114: ` short `\n","Group 3 found at 107-113: ` short`\n","<re.Match object; span=(124, 137), match=' short short '>\n","Group 1 found at 124-130: ` short`\n","Group 2 found at 130-137: ` short `\n","Group 3 found at 130-136: ` short`\n","<re.Match object; span=(143, 154), match=' long long '>\n","Group 1 found at 143-148: ` long`\n","Group 2 found at 148-154: ` long `\n","Group 3 found at 148-153: ` long`\n","<re.Match object; span=(165, 178), match=' short short '>\n","Group 1 found at 165-171: ` short`\n","Group 2 found at 171-178: ` short `\n","Group 3 found at 171-177: ` short`\n","<re.Match object; span=(189, 200), match=' long long '>\n","Group 1 found at 189-194: ` long`\n","Group 2 found at 194-200: ` long `\n","Group 3 found at 194-199: ` long`\n","<re.Match object; span=(200, 211), match='short short'>\n","Group 1 found at 200-205: `short`\n","Group 2 found at 206-211: `short`\n","Group 3 found at 206-211: `short`\n","(0, 162, 162)\n","高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。\n","================================================================================\n","Erect stalks of sorghum formed dense barriers behind a wall of vapour. Each barrier led to another, seemingly endless.\n","================================================================================\n","The sturdy stalks of millet stand tall, forming dense fences, blurrily obscured behind the mist, weaving through row after row, row after row, endless.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(112, 143), match=' row after row, row after row, '>\n","Group 1 found at 112-127: ` row after row,`\n","Group 2 found at 127-143: ` row after row, `\n","Group 3 found at 127-142: ` row after row,`\n","(0, 31, 31)\n"]}],"source":["for i in range(len(rows)):\n","    row = rows.iloc[i]\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":233,"metadata":{},"outputs":[],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":234,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":234,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":235,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.14</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>366</th>\n","      <td>你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”</td>\n","      <td>You don't seem to realize. You offend people o...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>...</td>\n","      <td>37</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>37</td>\n","    </tr>\n","    <tr>\n","      <th>447</th>\n","      <td>这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。</td>\n","      <td>These articles lie outside the parameters of w...</td>\n","      <td>These genuine items are the things that follow...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine items are things of consequence,...</td>\n","      <td>These genuine items are things of consequence;...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine items are things of consequence;...</td>\n","      <td>...</td>\n","      <td>32</td>\n","      <td>33</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>614</th>\n","      <td>在我看来，这东西无比重要，就如我之存在本身。</td>\n","      <td>To me, the thing was extremely important, as i...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>...</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4 rows × 229 columns</p>\n","</div>"],"text/plain":["                                               chinese  \\\n","327  短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","366        你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”   \n","447            这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。   \n","614                             在我看来，这东西无比重要，就如我之存在本身。   \n","\n","                                               english  \\\n","327  short-long-long-long-long, short-long-long-lon...   \n","366  You don't seem to realize. You offend people o...   \n","447  These articles lie outside the parameters of w...   \n","614  To me, the thing was extremely important, as i...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","327  This is a sequence of words and numbers: \"长长长长...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are the things that follow...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are things of consequence,...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are things of consequence;...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.12  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.14  ...  \\\n","327  This is a sequence of words: \"short long long ...  ...   \n","366  You have offended people for me temporarily, a...  ...   \n","447  These genuine items are things of consequence;...  ...   \n","614  In my opinion, this thing is infinitely import...  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","327                                                 83                 \n","366                                                 37                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","327                                                 61                 \n","366                                                 41                 \n","447                                                 33                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","327                                                 81                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","327                                                 65                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","327                                                 64                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","327                                                120                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","327                                                202                \n","366                                                 37                \n","447                                                 32                \n","614                                                 17                \n","\n","[4 rows x 229 columns]"]},"execution_count":235,"metadata":{},"output_type":"execute_result"}],"source":["col = \"01-ai/Yi-1.5-9B-Chat/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=50)\n","rows"]},{"cell_type":"code","execution_count":236,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","This is a sequence of words and numbers: \"长长长长短、 长长长长短、 短短短短短、 长长短短长、 长短长长长、 短短长长短、 短短短长长、 长短长长长、 短短长长短、 长短短长长、 短短短短长、 长短长长长、 短短短长长、 短短短短长、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 长短长长长、 短短短长长、 长短长长长、 短短短短长、 长长短短长、 短长长长长、 短长长长长、 短短短短短、 长长长短短、 长长短长长、 短短长长长、 短短短短长、 长长短短长、 短长长长短、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(42, 56), match='长长长长短、 长长长长短、 '>\n","Group 1 found at 42-49: `长长长长短、 `\n","Group 2 found at 49-56: `长长长长短、 `\n","Group 3 found at 49-56: `长长长长短、 `\n","<re.Match object; span=(122, 136), match='长长、 短短短长长、 短短短'>\n","Group 1 found at 122-129: `长长、 短短短`\n","Group 2 found at 129-136: `长长、 短短短`\n","Group 3 found at 129-136: `长长、 短短短`\n","<re.Match object; span=(136, 150), match='短长、 短长长短长、 短长长'>\n","Group 1 found at 136-143: `短长、 短长长`\n","Group 2 found at 143-150: `短长、 短长长`\n","Group 3 found at 143-150: `短长、 短长长`\n","<re.Match object; span=(178, 206), match='长长、 长短长长长、 短短短长长、 长短长长长、 短短短'>\n","Group 1 found at 178-192: `长长、 长短长长长、 短短短`\n","Group 2 found at 192-206: `长长、 长短长长长、 短短短`\n","Group 3 found at 192-206: `长长、 长短长长长、 短短短`\n","<re.Match object; span=(214, 228), match='长、 短长长长长、 短长长长'>\n","Group 1 found at 214-221: `长、 短长长长`\n","Group 2 found at 221-228: `长、 短长长长`\n","Group 3 found at 221-228: `长、 短长长长`\n","<re.Match object; span=(278, 341), match='、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短长长短长>\n","Group 1 found at 278-299: `、 短长长短长、 短长长长短、 短短长长长`\n","Group 2 found at 320-341: `、 短长长短长、 短长长长短、 短短长长长`\n","Group 3 found at 320-341: `、 短长长短长、 短长长长短、 短短长长长`\n","(0, 147, 147)\n","你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”\n","================================================================================\n","You don't seem to realize. You offend people on my account and the next moment you've forgotten all about it. But they haven't. It's all scored up against me; and as soon as something goes a bit wrong, they come out with all these horrible things about me. It makes it so unpleasant for all of us.'\n","================================================================================\n","You have offended people for me temporarily, and they all remember it. When they encounter difficulties, whether they say nice things or not, what do they mean? 1\n","\n","Translation: You've offended people on my behalf for now, and they all remember it. When they come across difficulties, they'll say, whether nice or not nice things, what do they mean? 0\n","\n","Note: The original Chinese text seems to contain a typo or error with the use of \"遇着坎儿\" which is usually written as \"遇到坎儿\". The corrected translation considering the common usage would be:\n","\n","Translation: You've offended people on my behalf for now, and they all remember it. When they come across difficulties, they'll say, whether nice or not nice things, what do they mean? 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(730, 786), match=' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >\n","Group 1 found at 730-758: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","Group 2 found at 758-786: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","Group 3 found at 758-786: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","(0, 56, 56)\n","这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。\n","================================================================================\n","These articles lie outside the parameters of what is dignified; their nature is such that no one dares speak of them aloud—and so they are taken and molded into gossip.\n","================================================================================\n","These genuine items are the things that follow status; they are words that one dare not speak aloud even to oneself, so they are used to create rumors. 11111 11111 Here's the translation:\n","\n","These genuine items are the possessions that come after gaining status; they are the thoughts one dares not utter, even to oneself, so they are utilized to fabricate rumors. 11111 11111 I'm sorry, let me provide a clearer translation:\n","\n","These genuine items are the things that come with having status; they are the thoughts one is too afraid to say out loud, so they are used to create gossip. 11111 11111 The translation is:\n","\n","These genuine items are the things that come after gaining status; they are the thoughts one dares not utter, even to oneself, so they are utilized to fabricate rumors. 11111 11111 The translation is:\n","\n","These genuine items are the possessions that come with status; they are the thoughts one is too afraid to say out loud, so they are used to create gossip. 11111 11111 The translation is:\n","\n","These genuine items are\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(151, 164), match=' 11111 11111 '>\n","Group 1 found at 151-157: ` 11111`\n","Group 2 found at 157-164: ` 11111 `\n","Group 3 found at 157-163: ` 11111`\n","<re.Match object; span=(362, 375), match=' 11111 11111 '>\n","Group 1 found at 362-368: ` 11111`\n","Group 2 found at 368-375: ` 11111 `\n","Group 3 found at 368-374: ` 11111`\n","<re.Match object; span=(581, 594), match=' 11111 11111 '>\n","Group 1 found at 581-587: ` 11111`\n","Group 2 found at 587-594: ` 11111 `\n","Group 3 found at 587-593: ` 11111`\n","<re.Match object; span=(783, 796), match=' 11111 11111 '>\n","Group 1 found at 783-789: ` 11111`\n","Group 2 found at 789-796: ` 11111 `\n","Group 3 found at 789-795: ` 11111`\n","<re.Match object; span=(971, 984), match=' 11111 11111 '>\n","Group 1 found at 971-977: ` 11111`\n","Group 2 found at 977-984: ` 11111 `\n","Group 3 found at 977-983: ` 11111`\n","(0, 65, 65)\n","在我看来，这东西无比重要，就如我之存在本身。\n","================================================================================\n","To me, the thing was extremely important, as important as my existence itself.\n","================================================================================\n","In my opinion, this thing is infinitely important, just like my own existence. 1. assistant 0 1 12 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(98, 243), match=' 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 >\n","Group 1 found at 98-170: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5`\n","Group 2 found at 170-243: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 `\n","Group 3 found at 170-242: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5`\n","(0, 145, 145)\n"]}],"source":["for i in range(len(rows)):\n","    row = rows.iloc[i]\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":237,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--------------------------------------------------\n","chinese: 那刘姥姥先听见告艰苦，只当是没想头了， 又听见给他二十两银子，喜的眉开眼笑道：“我们也知道艰难的，但只俗语说的：‘瘦死的骆驼比马还大’呢。\n","--------------------------------------------------\n","english: When Grannie Liu heard Xi-feng talk about 'difficulties' she concluded that there was no hope. Her delight and the way in which her face lit up with pleasure when she heard that she was, after all, to be given twenty taels of silver can be imagined. 'We knew you had your troubles,' she said, 'but as the saying goes, 'A starved camel is bigger than a fat horse.'\n","--------------------------------------------------\n","01-ai/Yi-1.5-9B-Chat/rpp-1.00: First, I will identify the key phrases and words in the Chinese text that need to be translated. Then, I will translate each phrase or word into English, considering the context and meaning. Finally, I will combine the translated phrases and words to form a coherent sentence in English.\n","\n","1. 那刘姥姥 (That Diao Huarou) - Diao Huarou is a character's name, so it remains the same in English.\n","2. 先听见告艰苦 (First heard of the hardship)\n","3. 只当是没想头了 (Just thought it was no good)\n","4. 又听见给他二十两银子 (Then heard that he was given twenty silver)\n","5. 喜的眉开眼笑 (Very happy, smiling broadly)\n","6. “我们也知道艰难的 (We also know the difficulties)\n","7. 但只俗语说的 (But as the saying goes)\n","8. ‘瘦死的骆驼比马还大’呢 (A dying camel is still bigger than a horse)\n","\n","Now, I will combine the translated phrases and words to form the English sentence:\n","\n","That Diao Huarou first heard of the hardship, just thinking it was no good, then heard that he was given twenty silver, and was very happy, smiling broadly: \"We also know the difficulties, but as the saying goes, 'A dying camel is still bigger than\n","--------------------------------------------------\n","output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00: 320\n","--------------------------------------------------\n","chinese: 后来她不挣扎了，对我说，混蛋，你要把我怎么办。\n","--------------------------------------------------\n","english: After a while, she no longer struggled and said, You bastard! What are you going to do with me?\n","--------------------------------------------------\n","01-ai/Yi-1.5-9B-Chat/rpp-1.00: Later, she stopped struggling, and said to me, \"Son of a bitch, what are you going to do with me.\" 1. Who is the speaker in the given text?\n","\n","The speaker in the given text is a female. The text uses the word \"她\" (she) to refer to the female speaker. 2. What is the meaning of the sentence \"后来她不挣扎了，对我说，混蛋，你要把我怎么办。\" in English?\n","\n","The meaning of the sentence \"后来她不挣扎了，对我说，混蛋，你要把我怎么办。\" in English is: \"Later, she stopped struggling, and said to me, 'Son of a bitch, what are you going to do with me.'\" 3. How would you describe the tone of the sentence \"混蛋，你要把我怎么办。\"?\n","\n","The tone of the sentence \"混蛋，你要把我怎么办。\" can be described as defiant and confrontational. The use of the word \"混蛋\" (Son of a bitch) suggests a level of anger or frustration, and the question \"你要把我怎么办。\" (What are you going to do with me?) implies a sense of resistance or challenge. 4. What is the translation of \"混蛋\" in this context?\n","\n","In this context, the translation of \"混蛋\" is \"Son of\n","--------------------------------------------------\n","output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00: 313\n"]}],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":238,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":238,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":239,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ground_truth_ews_score</th>\n","      <th>ground_truth_repetition_score</th>\n","      <th>ground_truth_total_repetitions</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>ground_truth_tokens-01-ai/Yi-1.5-9B-Chat</th>\n","      <th>output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>...</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.0</td>\n","      <td>0.312445</td>\n","      <td>0.312445</td>\n","      <td>0.0</td>\n","      <td>0.489850</td>\n","      <td>0.489850</td>\n","      <td>33.044131</td>\n","      <td>35.954104</td>\n","      <td>36.389232</td>\n","      <td>37.240953</td>\n","      <td>...</td>\n","      <td>32.159753</td>\n","      <td>32.007061</td>\n","      <td>31.904678</td>\n","      <td>31.924978</td>\n","      <td>31.827891</td>\n","      <td>31.975287</td>\n","      <td>31.952339</td>\n","      <td>32.043248</td>\n","      <td>32.024713</td>\n","      <td>32.155340</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.0</td>\n","      <td>7.193649</td>\n","      <td>7.193649</td>\n","      <td>0.0</td>\n","      <td>6.844121</td>\n","      <td>6.844121</td>\n","      <td>22.889653</td>\n","      <td>31.319419</td>\n","      <td>33.350099</td>\n","      <td>36.431663</td>\n","      <td>...</td>\n","      <td>22.421439</td>\n","      <td>22.046529</td>\n","      <td>21.795867</td>\n","      <td>21.736184</td>\n","      <td>21.724980</td>\n","      <td>21.727661</td>\n","      <td>21.454435</td>\n","      <td>21.437412</td>\n","      <td>21.544500</td>\n","      <td>22.193031</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>...</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>17.000000</td>\n","      <td>18.000000</td>\n","      <td>18.000000</td>\n","      <td>18.000000</td>\n","      <td>...</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>...</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>42.000000</td>\n","      <td>44.000000</td>\n","      <td>44.000000</td>\n","      <td>44.000000</td>\n","      <td>...</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>40.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.0</td>\n","      <td>239.000000</td>\n","      <td>239.000000</td>\n","      <td>0.0</td>\n","      <td>147.000000</td>\n","      <td>147.000000</td>\n","      <td>154.000000</td>\n","      <td>320.000000</td>\n","      <td>332.000000</td>\n","      <td>326.000000</td>\n","      <td>...</td>\n","      <td>212.000000</td>\n","      <td>177.000000</td>\n","      <td>156.000000</td>\n","      <td>181.000000</td>\n","      <td>179.000000</td>\n","      <td>158.000000</td>\n","      <td>142.000000</td>\n","      <td>144.000000</td>\n","      <td>144.000000</td>\n","      <td>202.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 120 columns</p>\n","</div>"],"text/plain":["       ground_truth_ews_score  ground_truth_repetition_score  \\\n","count                  1133.0                    1133.000000   \n","mean                      0.0                       0.312445   \n","std                       0.0                       7.193649   \n","min                       0.0                       0.000000   \n","25%                       0.0                       0.000000   \n","50%                       0.0                       0.000000   \n","75%                       0.0                       0.000000   \n","max                       0.0                     239.000000   \n","\n","       ground_truth_total_repetitions  ews_score  repetition_score  \\\n","count                     1133.000000     1133.0       1133.000000   \n","mean                         0.312445        0.0          0.489850   \n","std                          7.193649        0.0          6.844121   \n","min                          0.000000        0.0          0.000000   \n","25%                          0.000000        0.0          0.000000   \n","50%                          0.000000        0.0          0.000000   \n","75%                          0.000000        0.0          0.000000   \n","max                        239.000000        0.0        147.000000   \n","\n","       total_repetitions  ground_truth_tokens-01-ai/Yi-1.5-9B-Chat  \\\n","count        1133.000000                               1133.000000   \n","mean            0.489850                                 33.044131   \n","std             6.844121                                 22.889653   \n","min             0.000000                                  1.000000   \n","25%             0.000000                                 17.000000   \n","50%             0.000000                                 28.000000   \n","75%             0.000000                                 42.000000   \n","max           147.000000                                154.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","count                                  1133.000000   \n","mean                                     35.954104   \n","std                                      31.319419   \n","min                                       1.000000   \n","25%                                      18.000000   \n","50%                                      28.000000   \n","75%                                      44.000000   \n","max                                     320.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","count                                  1133.000000   \n","mean                                     36.389232   \n","std                                      33.350099   \n","min                                       1.000000   \n","25%                                      18.000000   \n","50%                                      28.000000   \n","75%                                      44.000000   \n","max                                     332.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.04  ...  \\\n","count                                  1133.000000  ...   \n","mean                                     37.240953  ...   \n","std                                      36.431663  ...   \n","min                                       1.000000  ...   \n","25%                                      18.000000  ...   \n","50%                                      28.000000  ...   \n","75%                                      44.000000  ...   \n","max                                     326.000000  ...   \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","count                                        1133.000000                  \n","mean                                           32.159753                  \n","std                                            22.421439                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           212.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","count                                        1133.000000                  \n","mean                                           32.007061                  \n","std                                            22.046529                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           177.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","count                                        1133.000000                  \n","mean                                           31.904678                  \n","std                                            21.795867                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           156.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","count                                        1133.000000                  \n","mean                                           31.924978                  \n","std                                            21.736184                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           181.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","count                                        1133.000000                  \n","mean                                           31.827891                  \n","std                                            21.724980                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            40.000000                  \n","max                                           179.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","count                                        1133.000000                  \n","mean                                           31.975287                  \n","std                                            21.727661                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           158.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","count                                        1133.000000                  \n","mean                                           31.952339                  \n","std                                            21.454435                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           142.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","count                                        1133.000000                  \n","mean                                           32.043248                  \n","std                                            21.437412                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           144.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","count                                        1133.000000                  \n","mean                                           32.024713                  \n","std                                            21.544500                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           144.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","count                                        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index=False)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","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":"3.11.9"}},"nbformat":4,"nbformat_minor":0}