diff --git "a/notebooks/00_Data Analysis.ipynb" "b/notebooks/00_Data Analysis.ipynb"
--- "a/notebooks/00_Data Analysis.ipynb"
+++ "b/notebooks/00_Data Analysis.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":106,"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":107,"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":108,"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":108,"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":109,"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":["Qwen/Qwen2-7B-Instruct 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":110,"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: 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 12.2 ms, sys: 15.1 ms, total: 27.2 ms\n","Wall time: 2.11 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":111,"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":112,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 100 columns):\n"," #   Column                                              Non-Null Count  Dtype \n","---  ------                                              --------------  ----- \n"," 0   chinese                                             1133 non-null   object\n"," 1   english                                             1133 non-null   object\n"," 2   01-ai/Yi-1.5-9B-Chat/rpp-1.00                       1133 non-null   object\n"," 3   01-ai/Yi-1.5-9B-Chat/rpp-1.02                       1133 non-null   object\n"," 4   01-ai/Yi-1.5-9B-Chat/rpp-1.04                       1133 non-null   object\n"," 5   01-ai/Yi-1.5-9B-Chat/rpp-1.06                       1133 non-null   object\n"," 6   01-ai/Yi-1.5-9B-Chat/rpp-1.08                       1133 non-null   object\n"," 7   01-ai/Yi-1.5-9B-Chat/rpp-1.10                       1133 non-null   object\n"," 8   01-ai/Yi-1.5-9B-Chat/rpp-1.12                       1133 non-null   object\n"," 9   01-ai/Yi-1.5-9B-Chat/rpp-1.14                       1133 non-null   object\n"," 10  01-ai/Yi-1.5-9B-Chat/rpp-1.16                       1133 non-null   object\n"," 11  01-ai/Yi-1.5-9B-Chat/rpp-1.18                       1133 non-null   object\n"," 12  01-ai/Yi-1.5-9B-Chat/rpp-1.20                       1133 non-null   object\n"," 13  Qwen/Qwen2-72B-Instruct/rpp-1.00                    1133 non-null   object\n"," 14  Qwen/Qwen2-72B-Instruct/rpp-1.02                    1133 non-null   object\n"," 15  Qwen/Qwen2-72B-Instruct/rpp-1.04                    1133 non-null   object\n"," 16  Qwen/Qwen2-72B-Instruct/rpp-1.06                    1133 non-null   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non-null   object\n"," 29  Qwen/Qwen2-7B-Instruct/rpp-1.00                     1133 non-null   object\n"," 30  Qwen/Qwen2-7B-Instruct/rpp-1.02                     1133 non-null   object\n"," 31  Qwen/Qwen2-7B-Instruct/rpp-1.04                     1133 non-null   object\n"," 32  Qwen/Qwen2-7B-Instruct/rpp-1.06                     1133 non-null   object\n"," 33  Qwen/Qwen2-7B-Instruct/rpp-1.08                     1133 non-null   object\n"," 34  Qwen/Qwen2-7B-Instruct/rpp-1.10                     1133 non-null   object\n"," 35  Qwen/Qwen2-7B-Instruct/rpp-1.12                     1133 non-null   object\n"," 36  Qwen/Qwen2-7B-Instruct/rpp-1.14                     1133 non-null   object\n"," 37  Qwen/Qwen2-7B-Instruct/rpp-1.16                     1133 non-null   object\n"," 38  Qwen/Qwen2-7B-Instruct/rpp-1.18                     1133 non-null   object\n"," 39  Qwen/Qwen2-7B-Instruct/rpp-1.20                     1133 non-null   object\n"," 40  Qwen/Qwen2-7B-Instruct/rpp-1.22                   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shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12      1133 non-null   object\n"," 53  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14      1133 non-null   object\n"," 54  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16      1133 non-null   object\n"," 55  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18      1133 non-null   object\n"," 56  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20      1133 non-null   object\n"," 57  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22      1133 non-null   object\n"," 58  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24      1133 non-null   object\n"," 59  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26      1133 non-null   object\n"," 60  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28      1133 non-null   object\n"," 61  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30      1133 non-null   object\n"," 62  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00  1133 non-null   object\n"," 63  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02  1133 non-null   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non-null   object\n"," 76  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  1133 non-null   object\n"," 77  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  1133 non-null   object\n"," 78  01-ai/Yi-1.5-9B-Chat/rpp-1.22                       1133 non-null   object\n"," 79  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02     1133 non-null   object\n"," 80  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04     1133 non-null   object\n"," 81  01-ai/Yi-1.5-9B-Chat/rpp-1.24                       1133 non-null   object\n"," 82  01-ai/Yi-1.5-9B-Chat/rpp-1.26                       1133 non-null   object\n"," 83  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06     1133 non-null   object\n"," 84  01-ai/Yi-1.5-9B-Chat/rpp-1.28                       1133 non-null   object\n"," 85  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.08     1133 non-null   object\n"," 86  01-ai/Yi-1.5-34B-Chat/rpp-1.00                      1133 non-null   object\n"," 87  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.10     1133 non-null   object\n"," 88  01-ai/Yi-1.5-9B-Chat/rpp-1.30                       1133 non-null   object\n"," 89  internlm/internlm2_5-7b-chat/rpp-1.00               1133 non-null   object\n"," 90  internlm/internlm2_5-7b-chat/rpp-1.02               1133 non-null   object\n"," 91  internlm/internlm2_5-7b-chat/rpp-1.04               1133 non-null   object\n"," 92  internlm/internlm2_5-7b-chat/rpp-1.06               1133 non-null   object\n"," 93  internlm/internlm2_5-7b-chat/rpp-1.08               1133 non-null   object\n"," 94  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.12     1133 non-null   object\n"," 95  internlm/internlm2_5-7b-chat/rpp-1.10               1133 non-null   object\n"," 96  internlm/internlm2_5-7b-chat/rpp-1.12               1133 non-null   object\n"," 97  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.14     1133 non-null   object\n"," 98  internlm/internlm2_5-7b-chat/rpp-1.14               1133 non-null   object\n"," 99  internlm/internlm2_5-7b-chat/rpp-1.16               1133 non-null   object\n","dtypes: object(100)\n","memory usage: 885.3+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":113,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," '01-ai/Yi-1.5-34B-Chat/rpp-1.00',\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"," 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'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/Llama3.1-8B-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"," 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'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":113,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":116,"metadata":{},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bc03a7bad4c9416488d365e00b42742e","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"176cc98d23624ad8915d39938369f3cd","version_major":2,"version_minor":0},"text/plain":["tokenizer.model:   0%|          | 0.00/1.03M [00:00<?, 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0.31586790820617083, 'rougeLsum': 0.31659574673209057}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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:254: 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","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\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-1.5-34B-Chat</td>\n","      <td>1.00</td>\n","      <td>0.375530</td>\n","      <td>0.105239</td>\n","      <td>0.358991</td>\n","      <td>0.0</td>\n","      <td>0.437776</td>\n","      <td>0.437776</td>\n","      <td>0.368670</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</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>2</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>3</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>4</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>...</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>93</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>94</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>95</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>96</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>97</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>98 rows × 10 columns</p>\n","</div>"],"text/plain":["                                        model   rpp    meteor    bleu_1  \\\n","0                       01-ai/Yi-1.5-34B-Chat  1.00  0.375530  0.105239   \n","1                        01-ai/Yi-1.5-9B-Chat  1.00  0.346373  0.093121   \n","2                        01-ai/Yi-1.5-9B-Chat  1.02  0.347119  0.091265   \n","3                        01-ai/Yi-1.5-9B-Chat  1.04  0.347188  0.090199   \n","4                        01-ai/Yi-1.5-9B-Chat  1.06  0.347595  0.090050   \n","..                                        ...   ...       ...       ...   \n","93  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.22  0.319397  0.080273   \n","94  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.24  0.318866  0.078780   \n","95  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.26  0.318051  0.077776   \n","96  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.28  0.315641  0.074712   \n","97  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   0.358991        0.0          0.437776           0.437776  0.368670   \n","1   0.332876        0.0          0.351280           0.351280  0.341256   \n","2   0.332589        0.0          0.264784           0.264784  0.343223   \n","3   0.331946        0.0          0.377758           0.377758  0.341686   \n","4   0.331282        0.0          0.468667           0.468667  0.340815   \n","..       ...        ...               ...                ...       ...   \n","93  0.308984        0.0          0.100618           0.100618  0.318015   \n","94  0.307286        0.0          0.082083           0.082083  0.317738   \n","95  0.306677        0.0          0.073257           0.073257  0.317046   \n","96  0.304695        0.0          0.057370           0.057370  0.314859   \n","97  0.303526        0.0          0.067961           0.067961  0.313562   \n","\n","    num_max_output_tokens  \n","0                       4  \n","1                       2  \n","2                       4  \n","3                       8  \n","4                       9  \n","..                    ...  \n","93                      0  \n","94                      0  \n","95                      0  \n","96                      0  \n","97                      0  \n","\n","[98 rows x 10 columns]"]},"execution_count":116,"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":117,"metadata":{},"outputs":[{"data":{"text/plain":["array(['01-ai/Yi-1.5-34B-Chat', '01-ai/Yi-1.5-9B-Chat',\n","       'Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n","       '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":117,"metadata":{},"output_type":"execute_result"}],"source":["models = metrics_df[\"model\"].unique()\n","models"]},{"cell_type":"code","execution_count":118,"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":140,"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":142,"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":143,"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":122,"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":123,"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-1.5-34B-Chat/rpp-1.00</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>...</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…… ��…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"There is... not... There is... not...\"</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 am I a thing!</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 × 212 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-1.5-34B-Chat/rpp-1.00 01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","193  \"There is... not... There is... not...\"       \"Yes…… no…… yes…… no……\"   \n","759                       What am I a thing!      What kind of thing am I!   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.02 01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\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-1.06 01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\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-1.10 01-ai/Yi-1.5-9B-Chat/rpp-1.12  ...  \\\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 212 columns]"]},"execution_count":123,"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":124,"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":124,"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":125,"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":125,"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":159,"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-1.5-34B-Chat/rpp-1.00</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>...</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>The text appears to be a sequence of character...</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>...</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 × 212 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-1.5-34B-Chat/rpp-1.00  \\\n","327  The text appears to be a sequence of character...   \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","    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 212 columns]"]},"execution_count":159,"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":160,"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":161,"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":162,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":162,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":165,"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-1.5-34B-Chat/rpp-1.00</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>...</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>\"There is... not... There is... not...\"</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>The text appears to be a sequence of character...</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>...</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 and straight stalks of sorghum form a...</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 × 212 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��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-1.5-34B-Chat/rpp-1.00  \\\n","193             \"There is... not... There is... not...\"   \n","327   The text appears to be a sequence of character...   \n","1045  The tall and straight stalks of sorghum form a...   \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","     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 212 columns]"]},"execution_count":165,"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":166,"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":167,"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":168,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":168,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":172,"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-1.5-34B-Chat/rpp-1.00</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>...</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>The text appears to be a sequence of character...</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>...</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 only thought of momentarily pleasing me b...</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 real things are the things behind the de...</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>...</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 extremely importa...</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 × 212 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-1.5-34B-Chat/rpp-1.00  \\\n","327  The text appears to be a sequence of character...   \n","366  \"You only thought of momentarily pleasing me b...   \n","447  These real things are the things behind the de...   \n","614  In my opinion, this thing is extremely importa...   \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","    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 212 columns]"]},"execution_count":172,"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":173,"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":175,"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":176,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":176,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":137,"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-1.5-34B-Chat</th>\n","      <th>output_tokens-01-ai/Yi-1.5-34B-Chat/rpp-1.00</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>...</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>0.231244</td>\n","      <td>0.231244</td>\n","      <td>33.044131</td>\n","      <td>35.268314</td>\n","      <td>33.044131</td>\n","      <td>35.954104</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>3.339904</td>\n","      <td>3.339904</td>\n","      <td>22.889653</td>\n","      <td>30.524109</td>\n","      <td>22.889653</td>\n","      <td>31.319419</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>2.000000</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>17.000000</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>43.000000</td>\n","      <td>42.000000</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>91.000000</td>\n","      <td>91.000000</td>\n","      <td>154.000000</td>\n","      <td>396.000000</td>\n","      <td>154.000000</td>\n","      <td>320.000000</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 × 112 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          0.231244   \n","std                          7.193649        0.0          3.339904   \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         91.000000   \n","\n","       total_repetitions  ground_truth_tokens-01-ai/Yi-1.5-34B-Chat  \\\n","count        1133.000000                                1133.000000   \n","mean            0.231244                                  33.044131   \n","std             3.339904                                  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            91.000000                                 154.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-34B-Chat/rpp-1.00  \\\n","count                                   1133.000000   \n","mean                                      35.268314   \n","std                                       30.524109   \n","min                                        2.000000   \n","25%                                       18.000000   \n","50%                                       28.000000   \n","75%                                       43.000000   \n","max                                      396.000000   \n","\n","       ground_truth_tokens-01-ai/Yi-1.5-9B-Chat  \\\n","count                               1133.000000   \n","mean                                  33.044131   \n","std                                   22.889653   \n","min                                    1.000000   \n","25%                                   17.000000   \n","50%                                   28.000000   \n","75%                                   42.000000   \n","max                                  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-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                                        1133.000000                 \n","mean                                           32.155340                 \n","std                                            22.193031                 \n","min                                             3.000000                 \n","25%                                            17.000000                 \n","50%                                            27.000000                 \n","75%                                            41.000000                 \n","max                                           202.000000                 \n","\n","[8 rows x 112 columns]"]},"execution_count":137,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"code","execution_count":138,"metadata":{},"outputs":[],"source":["metrics_df.to_csv(results_path.replace(\".csv\", \"_metrics.csv\"), 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}
+{"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': 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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���\" 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}