{"cells":[{"cell_type":"code","execution_count":210,"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":211,"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":212,"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":212,"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":213,"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\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","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":214,"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 9.65 ms, sys: 19.5 ms, total: 29.1 ms\n","Wall time: 1.87 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":215,"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":216,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 55 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","dtypes: object(55)\n","memory usage: 487.0+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":217,"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/Qwen2-7B-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"," '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":217,"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":218,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":219,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.3931693232556192, 'bleu_scores': {'bleu': 0.12273151341458781, 'precisions': [0.4199273774494459, 0.16226917210268393, 0.07941374663072777, 0.04192938209331652], 'brevity_penalty': 1.0, 'length_ratio': 1.0581649552832064, 'translation_length': 31946, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4422424373632814, 'rouge2': 0.19255208879947344, 'rougeL': 0.38436072285817197, 'rougeLsum': 0.384629860342585}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.02: {'meteor': 0.3925672197170406, 'bleu_scores': {'bleu': 0.12421056155279153, 'precisions': [0.4254972181364712, 0.16363093460734549, 0.08028819635962493, 0.042581432056249105], 'brevity_penalty': 1.0, 'length_ratio': 1.0359059291156012, 'translation_length': 31274, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4427056080159416, 'rouge2': 0.19219660001604671, 'rougeL': 0.38353574009053226, 'rougeLsum': 0.38400128515398857}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.04: {'meteor': 0.39235866930301305, 'bleu_scores': {'bleu': 0.12402693297052149, 'precisions': [0.4284005689164727, 0.16380901251551858, 0.07997907220090687, 0.04215992446800784], 'brevity_penalty': 1.0, 'length_ratio': 1.0247101689301092, 'translation_length': 30936, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4426208198446371, 'rouge2': 0.19199681779764224, 'rougeL': 0.3839514694136028, 'rougeLsum': 0.3841982412661236}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.06: {'meteor': 0.39099278006036825, 'bleu_scores': {'bleu': 0.1232450878300488, 'precisions': [0.4272606426093441, 0.16253786603837092, 0.07929176289453425, 0.04189893248806791], 'brevity_penalty': 1.0, 'length_ratio': 1.0216296787015569, 'translation_length': 30843, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44144425317154196, 'rouge2': 0.19133012055570608, 'rougeL': 0.38314456527389706, 'rougeLsum': 0.3834154006635245}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.08: {'meteor': 0.3919843215003691, 'bleu_scores': {'bleu': 0.12201600208223494, 'precisions': [0.4260587376277787, 0.16168047975203828, 0.07821366024518389, 0.04113935592107663], 'brevity_penalty': 1.0, 'length_ratio': 1.0207022192779065, 'translation_length': 30815, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44234072135783076, 'rouge2': 0.19220259288979116, 'rougeL': 0.3836061734813752, 'rougeLsum': 0.3839760269947858}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-7B-Instruct/rpp-1.00: {'meteor': 0.3773140250810713, 'bleu_scores': {'bleu': 0.11748158765428529, 'precisions': [0.4245090286015553, 0.1563922642478179, 0.07435367851292643, 0.038589981447124305], 'brevity_penalty': 1.0, 'length_ratio': 1.0052335210334549, 'translation_length': 30348, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42816771766738143, 'rouge2': 0.17894612960836942, 'rougeL': 0.3687767348793935, 'rougeLsum': 0.36863060006182824}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 533, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.02: {'meteor': 0.3776790505939881, 'bleu_scores': {'bleu': 0.11643158756980687, 'precisions': [0.4266733100813818, 0.15618528234157117, 0.07345809835123387, 0.03796757404425806], 'brevity_penalty': 0.997180530935826, 'length_ratio': 0.9971844981782048, 'translation_length': 30105, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42875758930848173, 'rouge2': 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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.3636360
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.3459840
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.3565750
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.3565750
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.3459840
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.2639010
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.2639010
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.2550750
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.2497790
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.2418360
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.2506620
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.2506620
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.2506620
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.2850840
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.2753750
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.2859660
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.2056490
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.1791700
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.2135920
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.2197710
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.2127101
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.3706970
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.3398060
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.3601060
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.3309800
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.3556930
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.3186230
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.3389230
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.3601060
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.3759930
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.3830540
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.3830540
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.4033540
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.4880850
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.3495150
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.2974400
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.2806710
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.1721090
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.1862310
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.8464251
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.8367171
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.8420121
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.3009710
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.4466020
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.2771400
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.2824360
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.1562220
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.1562220
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.1535750
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.1006180
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.2356580
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.0847310
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.1253310
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"],"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/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 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 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 0 \n","17 0 \n","18 0 \n","19 0 \n","20 1 \n","21 0 \n","22 0 \n","23 0 \n","24 0 \n","25 0 \n","26 0 \n","27 0 \n","28 0 \n","29 0 \n","30 0 \n","31 0 \n","32 0 \n","33 0 \n","34 0 \n","35 0 \n","36 0 \n","37 0 \n","38 0 \n","39 1 \n","40 1 \n","41 1 \n","42 0 \n","43 0 \n","44 0 \n","45 0 \n","46 0 \n","47 0 \n","48 0 \n","49 0 \n","50 0 \n","51 0 \n","52 0 "]},"execution_count":219,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":220,"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":221,"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/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
52shenzhi-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/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 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 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 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 0.373093 \n","6 0 0.373454 \n","7 0 0.373729 \n","8 0 0.373352 \n","9 0 0.370858 \n","10 0 0.368729 \n","11 0 0.367036 \n","12 0 0.364101 \n","13 0 0.362961 \n","14 0 0.359723 \n","15 0 0.355384 \n","16 0 0.354276 \n","17 0 0.350735 \n","18 0 0.344795 \n","19 0 0.340595 \n","20 1 0.337439 \n","21 0 0.352132 \n","22 0 0.352985 \n","23 0 0.351197 \n","24 0 0.351146 \n","25 0 0.349324 \n","26 0 0.348124 \n","27 0 0.346480 \n","28 0 0.346720 \n","29 0 0.344995 \n","30 0 0.343295 \n","31 0 0.342157 \n","32 0 0.340244 \n","33 0 0.337120 \n","34 0 0.337607 \n","35 0 0.336064 \n","36 0 0.335319 \n","37 0 0.323207 \n","38 0 0.322975 \n","39 1 0.271615 \n","40 1 0.271447 \n","41 1 0.271328 \n","42 0 0.321824 \n","43 0 0.318475 \n","44 0 0.318923 \n","45 0 0.318833 \n","46 0 0.319488 \n","47 0 0.318472 \n","48 0 0.316997 \n","49 0 0.317564 \n","50 0 0.314726 \n","51 0 0.314323 \n","52 0 0.312547 "]},"execution_count":221,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":223,"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":223,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":224,"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":234,"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":225,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":226,"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":227,"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/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04...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 ...\"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...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 59 columns

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"],"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/Qwen2-7B-Instruct/rpp-1.00 Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","193 \"Yes... No... Yes... No...\" \"Yes... No... Yes... No...\" \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 ... \\\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 59 columns]"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":228,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":230,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":231,"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":231,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":232,"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","
ews_scorerepetition_scoretotal_repetitionsoutput_tokens
count1133.01133.0000001133.0000001133.000000
mean0.05.8464255.84642533.958517
std0.0192.990061192.99006163.822891
min0.00.0000000.0000003.000000
25%0.00.0000000.00000017.000000
50%0.00.0000000.00000027.000000
75%0.00.0000000.00000042.000000
max0.06496.0000006496.0000002049.000000
\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"]},"execution_count":232,"metadata":{},"output_type":"execute_result"}],"source":["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}