{"cells":[{"cell_type":"code","execution_count":16,"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":17,"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":18,"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":18,"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":19,"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":20,"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 8.99 ms, sys: 15.6 ms, total: 24.6 ms\n","Wall time: 1.82 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":21,"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":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 58 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 16 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 18 Qwen/Qwen2-72B-Instruct/rpp-1.02 1133 non-null object\n"," 19 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 21 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 22 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 23 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 25 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 26 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 27 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 28 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 29 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 30 Qwen/Qwen2-72B-Instruct/rpp-1.04 1133 non-null object\n"," 31 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 32 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 33 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 34 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 35 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 36 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 38 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 39 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 41 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 43 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 45 Qwen/Qwen2-72B-Instruct/rpp-1.06 1133 non-null object\n"," 46 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 49 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 51 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 52 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 53 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 54 Qwen/Qwen2-72B-Instruct/rpp-1.08 1133 non-null object\n"," 55 Qwen/Qwen2-72B-Instruct/rpp-1.10 1133 non-null object\n"," 56 Qwen/Qwen2-72B-Instruct/rpp-1.12 1133 non-null object\n"," 57 internlm/internlm2_5-7b-chat/rpp-1.00 1133 non-null object\n","dtypes: object(58)\n","memory usage: 513.5+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\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/Qwen2-7B-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"," '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":23,"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":24,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"get_metrics() got an unexpected keyword argument 'max_new_tokens'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m metrics_df \u001b[38;5;241m=\u001b[39m \u001b[43mget_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m metrics_df\n","\u001b[0;31mTypeError\u001b[0m: get_metrics() got an unexpected keyword argument 'max_new_tokens'"]}],"source":["metrics_df = get_metrics(df, max_new_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n"," lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.36363600.387164
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.34598400.386853
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.35657500.386478
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.35657500.385133
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.34598400.386278
5Qwen/Qwen2-72B-Instruct1.100.3910100.1206100.3819630.00.3768760.37687600.384827
6Qwen/Qwen2-72B-Instruct1.120.3899890.1183830.3817370.00.4068840.40688400.383349
7Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
8Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
9Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
10Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
11Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
12Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
13Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
14Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
15Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
16Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
17Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
18Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
19Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
20Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
21Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
22Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
37shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
38shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
53shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
54shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.12533100.312547
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-72B-Instruct 1.10 0.391010 0.120610 \n","6 Qwen/Qwen2-72B-Instruct 1.12 0.389989 0.118383 \n","7 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","8 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","9 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","10 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","11 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","12 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","13 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","14 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","15 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","16 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","17 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","18 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","19 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","20 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","21 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","22 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","37 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","38 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","53 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","54 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.381963 0.0 0.376876 0.376876 \n","6 0.381737 0.0 0.406884 0.406884 \n","7 0.368777 0.0 0.263901 0.263901 \n","8 0.369318 0.0 0.263901 0.263901 \n","9 0.368963 0.0 0.255075 0.255075 \n","10 0.367135 0.0 0.249779 0.249779 \n","11 0.364109 0.0 0.241836 0.241836 \n","12 0.360194 0.0 0.250662 0.250662 \n","13 0.358556 0.0 0.250662 0.250662 \n","14 0.356445 0.0 0.250662 0.250662 \n","15 0.354277 0.0 0.285084 0.285084 \n","16 0.350097 0.0 0.275375 0.275375 \n","17 0.346421 0.0 0.285966 0.285966 \n","18 0.345115 0.0 0.205649 0.205649 \n","19 0.339449 0.0 0.179170 0.179170 \n","20 0.334751 0.0 0.213592 0.213592 \n","21 0.330192 0.0 0.219771 0.219771 \n","22 0.326872 0.0 0.212710 0.212710 \n","23 0.345662 0.0 0.370697 0.370697 \n","24 0.345484 0.0 0.339806 0.339806 \n","25 0.344864 0.0 0.360106 0.360106 \n","26 0.343229 0.0 0.330980 0.330980 \n","27 0.342915 0.0 0.355693 0.355693 \n","28 0.341288 0.0 0.318623 0.318623 \n","29 0.340665 0.0 0.338923 0.338923 \n","30 0.340098 0.0 0.360106 0.360106 \n","31 0.338469 0.0 0.375993 0.375993 \n","32 0.337621 0.0 0.383054 0.383054 \n","33 0.335723 0.0 0.383054 0.383054 \n","34 0.334642 0.0 0.403354 0.403354 \n","35 0.332546 0.0 0.488085 0.488085 \n","36 0.330630 0.0 0.349515 0.349515 \n","37 0.328182 0.0 0.297440 0.297440 \n","38 0.327286 0.0 0.280671 0.280671 \n","39 0.315822 0.0 0.172109 0.172109 \n","40 0.315877 0.0 0.186231 0.186231 \n","41 0.316605 0.0 5.846425 5.846425 \n","42 0.315051 0.0 5.836717 5.836717 \n","43 0.314946 0.0 5.842012 5.842012 \n","44 0.315208 0.0 0.300971 0.300971 \n","45 0.313913 0.0 0.446602 0.446602 \n","46 0.313531 0.0 0.277140 0.277140 \n","47 0.313136 0.0 0.282436 0.282436 \n","48 0.311101 0.0 0.156222 0.156222 \n","49 0.310410 0.0 0.156222 0.156222 \n","50 0.308024 0.0 0.153575 0.153575 \n","51 0.307217 0.0 0.100618 0.100618 \n","52 0.306926 0.0 0.235658 0.235658 \n","53 0.304750 0.0 0.084731 0.084731 \n","54 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.387164 \n","1 0 0.386853 \n","2 0 0.386478 \n","3 0 0.385133 \n","4 0 0.386278 \n","5 0 0.384827 \n","6 0 0.383349 \n","7 0 0.373093 \n","8 0 0.373454 \n","9 0 0.373729 \n","10 0 0.373352 \n","11 0 0.370858 \n","12 0 0.368729 \n","13 0 0.367036 \n","14 0 0.364101 \n","15 0 0.362961 \n","16 0 0.359723 \n","17 0 0.355384 \n","18 0 0.354276 \n","19 0 0.350735 \n","20 0 0.344795 \n","21 0 0.340595 \n","22 1 0.337439 \n","23 0 0.352132 \n","24 0 0.352985 \n","25 0 0.351197 \n","26 0 0.351146 \n","27 0 0.349324 \n","28 0 0.348124 \n","29 0 0.346480 \n","30 0 0.346720 \n","31 0 0.344995 \n","32 0 0.343295 \n","33 0 0.342157 \n","34 0 0.340244 \n","35 0 0.337120 \n","36 0 0.337607 \n","37 0 0.336064 \n","38 0 0.335319 \n","39 0 0.323207 \n","40 0 0.322975 \n","41 1 0.271615 \n","42 1 0.271447 \n","43 1 0.271328 \n","44 0 0.321824 \n","45 0 0.318475 \n","46 0 0.318923 \n","47 0 0.318833 \n","48 0 0.319488 \n","49 0 0.318472 \n","50 0 0.316997 \n","51 0 0.317564 \n","52 0 0.314726 \n","53 0 0.314323 \n","54 0 0.312547 "]},"execution_count":288,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":290,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\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, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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[\"total_repetitions\"], label=model)\n","\n","\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, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-72B-Instruct/rpp-1.02Qwen/Qwen2-72B-Instruct/rpp-1.04Qwen/Qwen2-72B-Instruct/rpp-1.06Qwen/Qwen2-72B-Instruct/rpp-1.08Qwen/Qwen2-72B-Instruct/rpp-1.10Qwen/Qwen2-72B-Instruct/rpp-1.12Qwen/Qwen2-7B-Instruct/rpp-1.00...shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30ews_scorerepetition_scoretotal_repetitionsoutput_tokens
193“有…… 没有…… 有…… 没有……'Yes . . . no . . . yes . . . no . . .\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"Yes... No... Yes... No...\"...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 61 columns

\n","
"],"text/plain":[" chinese english \\\n","193 “有…… 没有…… 有…… 没有…… 'Yes . . . no . . . yes . . . no . . . \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.02 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.04 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.06 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.08 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.10 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.12 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 ... \\\n","193 \"Yes... No... Yes... No...\" ... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score \\\n","193 Yes... No... Yes... No... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","193 6496 6496 2049 \n","\n","[1 rows x 61 columns]"]},"execution_count":295,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["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! 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! 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! 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! 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! 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! 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! 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! 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\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 159-3407: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":299,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ews_scorerepetition_scoretotal_repetitionsoutput_tokensground_truth_tokens-Qwen/Qwen2-72B-Instructground_truth_tokens-Qwen/Qwen2-7B-Instructground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chatground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat
count1133.01133.0000001133.0000001133.0000001133.0000001133.0000001133.0000001133.000000
mean0.05.8464255.84642533.95851729.45807629.45807629.43248032.805825
std0.0192.990061192.99006163.82289120.12665920.12665920.07666621.906509
min0.00.0000000.0000003.0000001.0000001.0000001.0000002.000000
25%0.00.0000000.00000017.00000016.00000016.00000016.00000018.000000
50%0.00.0000000.00000027.00000025.00000025.00000025.00000027.000000
75%0.00.0000000.00000042.00000038.00000038.00000038.00000042.000000
max0.06496.0000006496.0000002049.000000135.000000135.000000135.000000149.000000
\n","
"],"text/plain":[" ews_score repetition_score total_repetitions output_tokens \\\n","count 1133.0 1133.000000 1133.000000 1133.000000 \n","mean 0.0 5.846425 5.846425 33.958517 \n","std 0.0 192.990061 192.990061 63.822891 \n","min 0.0 0.000000 0.000000 3.000000 \n","25% 0.0 0.000000 0.000000 17.000000 \n","50% 0.0 0.000000 0.000000 27.000000 \n","75% 0.0 0.000000 0.000000 42.000000 \n","max 0.0 6496.000000 6496.000000 2049.000000 \n","\n"," ground_truth_tokens-Qwen/Qwen2-72B-Instruct \\\n","count 1133.000000 \n","mean 29.458076 \n","std 20.126659 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-Qwen/Qwen2-7B-Instruct \\\n","count 1133.000000 \n","mean 29.458076 \n","std 20.126659 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat \\\n","count 1133.000000 \n","mean 29.432480 \n","std 20.076666 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat \n","count 1133.000000 \n","mean 32.805825 \n","std 21.906509 \n","min 2.000000 \n","25% 18.000000 \n","50% 27.000000 \n","75% 42.000000 \n","max 149.000000 "]},"execution_count":300,"metadata":{},"output_type":"execute_result"}],"source":["for model in models:\n"," df[f\"ground_truth_tokens-{model}\"] = df[\"english\"].apply(\n"," lambda x: len(tokenizers[model](x)[\"input_ids\"])\n"," )\n","\n","df.describe()"]}],"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}