dh-mc commited on
Commit
b83b354
1 Parent(s): 1ebd37e

ready for llama-3.1-8b

Browse files
llm_toolkit/eval_rpp.py CHANGED
@@ -64,7 +64,9 @@ if is_cuda:
64
  print(f"(2) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
65
  print(f"{start_gpu_memory} GB of memory reserved.")
66
 
67
- datasets = load_translation_dataset(data_path, tokenizer, using_chat_template=using_chat_template)
 
 
68
 
69
  if len(sys.argv) > 1:
70
  num = int(sys.argv[1])
@@ -83,7 +85,12 @@ def on_repetition_penalty_step_completed(model_name, predictions):
83
  predictions,
84
  )
85
 
86
- metrics = calc_metrics(datasets["test"]["english"], predictions, datasets["test"]["chinese"], debug=True)
 
 
 
 
 
87
  print(f"{model_name} metrics: {metrics}")
88
 
89
 
 
64
  print(f"(2) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
65
  print(f"{start_gpu_memory} GB of memory reserved.")
66
 
67
+ datasets = load_translation_dataset(
68
+ data_path, tokenizer, using_chat_template=using_chat_template
69
+ )
70
 
71
  if len(sys.argv) > 1:
72
  num = int(sys.argv[1])
 
85
  predictions,
86
  )
87
 
88
+ metrics = calc_metrics(
89
+ datasets["test"]["english"],
90
+ predictions,
91
+ datasets["test"]["chinese"],
92
+ debug=True,
93
+ )
94
  print(f"{model_name} metrics: {metrics}")
95
 
96
 
llm_toolkit/translation_utils.py CHANGED
@@ -118,7 +118,9 @@ def get_few_shot_prompt(dataset, num_shots=5):
118
  return translation_prompt
119
 
120
 
121
- def load_translation_dataset(data_path, tokenizer=None, num_shots=0, for_openai=False, using_chat_template=True):
 
 
122
  train_data_file = data_path.replace(".tsv", "-train.tsv")
123
  test_data_file = data_path.replace(".tsv", "-test.tsv")
124
 
@@ -185,10 +187,14 @@ def load_translation_dataset(data_path, tokenizer=None, num_shots=0, for_openai=
185
  )
186
  texts.append(text)
187
  else:
188
- prompt = tokenizer.apply_chat_template(
189
- messages, tokenize=False, add_generation_prompt=True
190
- ) if using_chat_template else prompt
191
-
 
 
 
 
192
  prompts.append(prompt)
193
  texts.append(prompt + output + tokenizer.eos_token)
194
 
@@ -242,7 +248,7 @@ def detect_repetition_scores(row, col, debug=False):
242
  def count_chinese_characters(text):
243
  if isinstance(text, str) is False:
244
  return 0
245
-
246
  # Define a regular expression pattern for Chinese characters
247
  chinese_char_pattern = r"[\u4e00-\u9fff]"
248
 
@@ -266,7 +272,14 @@ def get_metrics(df, max_output_tokens=2048, variant="rpp", existing_metrics_df=N
266
  metrics_df.reset_index(inplace=True)
267
  metrics_df = metrics_df.drop(columns=["index"])
268
 
269
- models = metrics_df["model"].unique()
 
 
 
 
 
 
 
270
  print(models)
271
 
272
  tokenizers = {model: load_tokenizer(model) for model in models}
@@ -295,15 +308,27 @@ def get_metrics(df, max_output_tokens=2048, variant="rpp", existing_metrics_df=N
295
  df[new_col] = df["chinese"].apply(count_chinese_characters)
296
 
297
  for col in columns:
 
298
  if existing_metrics_df is not None:
299
- print(f"Using existing metrics for {col}")
300
  parts = col.split(f"/{variant}-")
 
 
 
 
301
  result = existing_metrics_df[
302
- (existing_metrics_df["model"] == parts[0])
303
- & (existing_metrics_df[variant] == int(parts[1]))
304
  ]
305
- metrics = result.to_dict("records")[0]
306
- else:
 
 
 
 
 
 
 
 
 
307
  print(f"Calculating metrics for {col}")
308
  metrics = calc_metrics(
309
  df["english"], df[col], sources=df["chinese"], debug=True
@@ -345,7 +370,11 @@ def get_metrics(df, max_output_tokens=2048, variant="rpp", existing_metrics_df=N
345
  translation_completeness.append(1 - df[new_col].sum() / len(df))
346
 
347
  new_col = f"output_tokens-{col}"
348
- df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)["input_ids"]))
 
 
 
 
349
 
350
  num_max_output_tokens.append(
351
  count_entries_with_max_tokens(df[new_col], max_output_tokens)
 
118
  return translation_prompt
119
 
120
 
121
+ def load_translation_dataset(
122
+ data_path, tokenizer=None, num_shots=0, for_openai=False, using_chat_template=True
123
+ ):
124
  train_data_file = data_path.replace(".tsv", "-train.tsv")
125
  test_data_file = data_path.replace(".tsv", "-test.tsv")
126
 
 
187
  )
188
  texts.append(text)
189
  else:
190
+ prompt = (
191
+ tokenizer.apply_chat_template(
192
+ messages, tokenize=False, add_generation_prompt=True
193
+ )
194
+ if using_chat_template
195
+ else prompt
196
+ )
197
+
198
  prompts.append(prompt)
199
  texts.append(prompt + output + tokenizer.eos_token)
200
 
 
248
  def count_chinese_characters(text):
249
  if isinstance(text, str) is False:
250
  return 0
251
+
252
  # Define a regular expression pattern for Chinese characters
253
  chinese_char_pattern = r"[\u4e00-\u9fff]"
254
 
 
272
  metrics_df.reset_index(inplace=True)
273
  metrics_df = metrics_df.drop(columns=["index"])
274
 
275
+ models = [
276
+ model
277
+ for model in metrics_df["model"].unique()
278
+ if ("/" in model or "gpt" in model)
279
+ and "ground_truth_" not in model
280
+ and "count_" not in model
281
+ and "output_" not in model
282
+ ]
283
  print(models)
284
 
285
  tokenizers = {model: load_tokenizer(model) for model in models}
 
308
  df[new_col] = df["chinese"].apply(count_chinese_characters)
309
 
310
  for col in columns:
311
+ metrics = None
312
  if existing_metrics_df is not None:
 
313
  parts = col.split(f"/{variant}-")
314
+ if len(parts) == 1:
315
+ break
316
+ # print(parts)
317
+ val = float(parts[1]) if variant == "rpp" else int(parts[1])
318
  result = existing_metrics_df[
319
+ existing_metrics_df["model"] == parts[0].split("/checkpoint")[0]
 
320
  ]
321
+
322
+ for i, row in result.iterrows():
323
+ # print(i, row[variant], val)
324
+ if row[variant] == val:
325
+ print(f"Using existing metrics for {col}")
326
+ metrics = row.to_dict()
327
+ # print(metrics)
328
+ break
329
+ # metrics = result.to_dict("records")[0]
330
+
331
+ if metrics is None:
332
  print(f"Calculating metrics for {col}")
333
  metrics = calc_metrics(
334
  df["english"], df[col], sources=df["chinese"], debug=True
 
370
  translation_completeness.append(1 - df[new_col].sum() / len(df))
371
 
372
  new_col = f"output_tokens-{col}"
373
+ df[new_col] = df[col].apply(
374
+ lambda x: (
375
+ len(tokenizers[model](x)["input_ids"]) if isinstance(x, str) else 0
376
+ )
377
+ )
378
 
379
  num_max_output_tokens.append(
380
  count_entries_with_max_tokens(df[new_col], max_output_tokens)
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1
- {"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"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: d:\\code\\projects\\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":3,"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: d:\\code\\projects\\rapget-translation\\.env\n"]},{"data":{"text/plain":["True"]},"execution_count":3,"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":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["01-ai/Yi-1.5-9B-Chat None True datasets/mac/mac.tsv results/mac-results_few_shots_4bit.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":5,"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+cu124\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: C:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, bitsandbytes, 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: C:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: total: 0 ns\n","Wall time: 8.35 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":6,"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":"stderr","output_type":"stream","text":["c:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\\threadpoolctl.py:1214: RuntimeWarning: \n","Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n","the same time. Both libraries are known to be incompatible and this\n","can cause random crashes or deadlocks on Linux when loaded in the\n","same Python program.\n","Using threadpoolctl may cause crashes or deadlocks. For more\n","information and possible workarounds, please see\n"," https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n","\n"," warnings.warn(msg, RuntimeWarning)\n","[nltk_data] Downloading package wordnet to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package wordnet is already up-to-date!\n","[nltk_data] Downloading package punkt to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package punkt is already up-to-date!\n","[nltk_data] Downloading package omw-1.4 to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package omw-1.4 is already up-to-date!\n"]},{"name":"stdout","output_type":"stream","text":["loading: d:\\code\\projects\\rapget-translation\\eval_modules\\calc_repetitions.py\n","loading d:\\code\\projects\\rapget-translation\\llm_toolkit\\translation_utils.py\n"]},{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package wordnet to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package wordnet is already up-to-date!\n","[nltk_data] Downloading package punkt to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package punkt is already up-to-date!\n","[nltk_data] Downloading package omw-1.4 to\n","[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n","[nltk_data] Package omw-1.4 is already up-to-date!\n"]},{"name":"stdout","output_type":"stream","text":["CUDA is available, we have found 1 GPU(s)\n","NVIDIA GeForce RTX 4080 Laptop GPU\n","CUDA version: 12.4\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":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['chinese', 'english'],\n"," num_rows: 4528\n"," })\n"," test: Dataset({\n"," features: ['chinese', 'english'],\n"," num_rows: 1133\n"," })\n","})\n"]}],"source":["datasets = load_translation_dataset(data_path)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["os.getenv(\"OPENAI_MODEL\")\n","base_url = os.getenv(\"OPENAI_BASE_URL\") or None"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--------------------------------------------------\n","chinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\n","--------------------------------------------------\n","english: When Grannie Liu heard Xi-feng talk about 'difficulties' she concluded that there was no hope. Her delight and the way in which her face lit up with pleasure when she heard that she was, after all, to be given twenty taels of silver can be imagined. 'We knew you had your troubles,' she said, 'but as the saying goes, 'A starved camel is bigger than a fat horse.'\n","--------------------------------------------------\n","chinese: 后来她不挣扎了,对我说,混蛋,你要把我怎么办。\n","--------------------------------------------------\n","english: After a while, she no longer struggled and said, You bastard! What are you going to do with me?\n"]}],"source":["eval_dataset = datasets[\"test\"].select([260, 908])\n","print_row_details(eval_dataset.to_pandas(), range(len(eval_dataset)))"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\n","\n","Example Translations:\n","Chinese: 全仗着狐仙搭救。\n","English: Because I was protected by a fox fairy.\n","Chinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\n","English: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\n","Chinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\n","English: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\n","Chinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\n","English: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\n","Chinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\n","English: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\n","\n","Chinese: {input}\n","English:\n"]}],"source":["translation_prompt = get_few_shot_prompt(datasets[\"train\"], num_shots=5)\n","print(translation_prompt)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n","\u001b[0m{\n"," \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n","}\n","\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n","\u001b[0m{\n"," \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n","\u001b[0m[outputs]\n","\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n","\u001b[0m{\n"," \"prompts\": [\n"," \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\\nEnglish:\"\n"," ]\n","}\n","\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [1.45s] Exiting LLM run with output:\n","\u001b[0m{\n"," \"generations\": [\n"," [\n"," {\n"," \"text\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know about difficulties, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n"," \"generation_info\": {\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ChatGeneration\",\n"," \"message\": {\n"," \"lc\": 1,\n"," \"type\": \"constructor\",\n"," \"id\": [\n"," \"langchain\",\n"," \"schema\",\n"," \"messages\",\n"," \"AIMessage\"\n"," ],\n"," \"kwargs\": {\n"," \"content\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know about difficulties, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n"," \"response_metadata\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 56,\n"," \"prompt_tokens\": 484,\n"," \"total_tokens\": 540\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_0f03d4f0ee\",\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ai\",\n"," \"id\": \"run-1c7d8c24-e2d6-4ba3-b87a-16101fb1ce80-0\",\n"," \"usage_metadata\": {\n"," \"input_tokens\": 484,\n"," \"output_tokens\": 56,\n"," \"total_tokens\": 540\n"," },\n"," \"tool_calls\": [],\n"," \"invalid_tool_calls\": []\n"," }\n"," }\n"," }\n"," ]\n"," ],\n"," \"llm_output\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 56,\n"," \"prompt_tokens\": 484,\n"," \"total_tokens\": 540\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_0f03d4f0ee\"\n"," },\n"," \"run\": null\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [1.46s] Exiting Chain run with output:\n","\u001b[0m[outputs]\n"]},{"data":{"text/plain":["'That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \"We also know about difficulties, but as the saying goes: \\'A dead camel is still bigger than a horse.\\'\"'"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["from langchain_core.globals import set_debug\n","\n","set_debug(True)\n","\n","translate_via_openai(eval_dataset[\"chinese\"][0], translation_prompt, max_tokens=max_new_tokens)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["datasets[\"test\"] = eval_dataset"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":[" 0%| | 0/2 [00:00<?, ?it/s]"]},{"name":"stdout","output_type":"stream","text":["\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n","\u001b[0m{\n"," \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n","}\n","\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n","\u001b[0m{\n"," \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n","\u001b[0m[outputs]\n","\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n","\u001b[0m{\n"," \"prompts\": [\n"," \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走���叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\\nEnglish:\"\n"," ]\n","}\n"]},{"name":"stderr","output_type":"stream","text":[" 50%|█████ | 1/2 [00:02<00:02, 2.31s/it]"]},{"name":"stdout","output_type":"stream","text":["\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [1.27s] Exiting LLM run with output:\n","\u001b[0m{\n"," \"generations\": [\n"," [\n"," {\n"," \"text\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know it's difficult, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n"," \"generation_info\": {\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ChatGeneration\",\n"," \"message\": {\n"," \"lc\": 1,\n"," \"type\": \"constructor\",\n"," \"id\": [\n"," \"langchain\",\n"," \"schema\",\n"," \"messages\",\n"," \"AIMessage\"\n"," ],\n"," \"kwargs\": {\n"," \"content\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know it's difficult, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n"," \"response_metadata\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 56,\n"," \"prompt_tokens\": 484,\n"," \"total_tokens\": 540\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_9b0abffe81\",\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ai\",\n"," \"id\": \"run-68581936-c5f8-4d63-a40a-9ae04e88d234-0\",\n"," \"usage_metadata\": {\n"," \"input_tokens\": 484,\n"," \"output_tokens\": 56,\n"," \"total_tokens\": 540\n"," },\n"," \"tool_calls\": [],\n"," \"invalid_tool_calls\": []\n"," }\n"," }\n"," }\n"," ]\n"," ],\n"," \"llm_output\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 56,\n"," \"prompt_tokens\": 484,\n"," \"total_tokens\": 540\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_9b0abffe81\"\n"," },\n"," \"run\": null\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [1.28s] Exiting Chain run with output:\n","\u001b[0m[outputs]\n","\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n","\u001b[0m{\n"," \"input\": \"后来她不挣扎了,对我说,混蛋,你要把我怎么办。\"\n","}\n","\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n","\u001b[0m{\n"," \"input\": \"后来她不挣扎了,对我说,混蛋,你要把我怎么办。\"\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n","\u001b[0m[outputs]\n","\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n","\u001b[0m{\n"," \"prompts\": [\n"," \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 后来她不挣扎了,对我说,混蛋,你要把我怎么办。\\nEnglish:\"\n"," ]\n","}\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 2/2 [00:04<00:00, 2.12s/it]"]},{"name":"stdout","output_type":"stream","text":["\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [908ms] Exiting LLM run with output:\n","\u001b[0m{\n"," \"generations\": [\n"," [\n"," {\n"," \"text\": \"Later, she stopped struggling and said to me, \\\"Bastard, what are you going to do with me?\\\"\",\n"," \"generation_info\": {\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ChatGeneration\",\n"," \"message\": {\n"," \"lc\": 1,\n"," \"type\": \"constructor\",\n"," \"id\": [\n"," \"langchain\",\n"," \"schema\",\n"," \"messages\",\n"," \"AIMessage\"\n"," ],\n"," \"kwargs\": {\n"," \"content\": \"Later, she stopped struggling and said to me, \\\"Bastard, what are you going to do with me?\\\"\",\n"," \"response_metadata\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 24,\n"," \"prompt_tokens\": 433,\n"," \"total_tokens\": 457\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_611b667b19\",\n"," \"finish_reason\": \"stop\",\n"," \"logprobs\": null\n"," },\n"," \"type\": \"ai\",\n"," \"id\": \"run-e4bb10fb-f7c4-4440-82ba-c13a1a82bc00-0\",\n"," \"usage_metadata\": {\n"," \"input_tokens\": 433,\n"," \"output_tokens\": 24,\n"," \"total_tokens\": 457\n"," },\n"," \"tool_calls\": [],\n"," \"invalid_tool_calls\": []\n"," }\n"," }\n"," }\n"," ]\n"," ],\n"," \"llm_output\": {\n"," \"token_usage\": {\n"," \"completion_tokens\": 24,\n"," \"prompt_tokens\": 433,\n"," \"total_tokens\": 457\n"," },\n"," \"model_name\": \"gpt-4o-mini-2024-07-18\",\n"," \"system_fingerprint\": \"fp_611b667b19\"\n"," },\n"," \"run\": null\n","}\n","\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [918ms] Exiting Chain run with output:\n","\u001b[0m[outputs]\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["predictions = eval_openai(5, datasets)"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["['That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \"We also know it\\'s difficult, but as the saying goes: \\'A dead camel is still bigger than a horse.\\'\"', 'Later, she stopped struggling and said to me, \"Bastard, what are you going to do with me?\"']\n"]}],"source":["print(predictions)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/plain":["{'meteor': 0.5376810911615811,\n"," 'bleu_scores': {'bleu': 0.16133991724232039,\n"," 'precisions': [0.5454545454545454,\n"," 0.26666666666666666,\n"," 0.1643835616438356,\n"," 0.09859154929577464],\n"," 'brevity_penalty': 0.7322097138745853,\n"," 'length_ratio': 0.7623762376237624,\n"," 'translation_length': 77,\n"," 'reference_length': 101},\n"," 'rouge_scores': {'rouge1': 0.5594202898550725,\n"," 'rouge2': 0.362051015096304,\n"," 'rougeL': 0.5246376811594203,\n"," 'rougeLsum': 0.5246376811594203},\n"," 'accuracy': 0.0}"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["calc_metrics(eval_dataset[\"english\"], predictions)"]}],"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}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {
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+ "elapsed": 476,
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+ "status": "ok",
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+ "timestamp": 1720679526275,
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+ "user": {
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+ "displayName": "HUANG DONGHAO _",
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+ "userId": "00977795705617022768"
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+ },
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+ "user_tz": -480
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+ },
17
+ "id": "uWKRSV6eZsCn"
18
+ },
19
+ "outputs": [],
20
+ "source": [
21
+ "%load_ext autoreload\n",
22
+ "%autoreload 2"
23
+ ]
24
+ },
25
+ {
26
+ "cell_type": "code",
27
+ "execution_count": 2,
28
+ "metadata": {
29
+ "application/vnd.databricks.v1+cell": {
30
+ "cellMetadata": {
31
+ "byteLimit": 2048000,
32
+ "rowLimit": 10000
33
+ },
34
+ "inputWidgets": {},
35
+ "nuid": "6d394937-6c99-4a7c-9d32-7600a280032f",
36
+ "showTitle": false,
37
+ "title": ""
38
+ },
39
+ "colab": {
40
+ "base_uri": "https://localhost:8080/"
41
+ },
42
+ "executionInfo": {
43
+ "elapsed": 5,
44
+ "status": "ok",
45
+ "timestamp": 1720679529345,
46
+ "user": {
47
+ "displayName": "HUANG DONGHAO _",
48
+ "userId": "00977795705617022768"
49
+ },
50
+ "user_tz": -480
51
+ },
52
+ "id": "G5pNu3zgZBrL",
53
+ "outputId": "160a554f-fb08-4aa0-bc00-0422fb7c1fac"
54
+ },
55
+ "outputs": [
56
+ {
57
+ "name": "stdout",
58
+ "output_type": "stream",
59
+ "text": [
60
+ "workding dir: d:\\code\\projects\\rapget-translation\n"
61
+ ]
62
+ }
63
+ ],
64
+ "source": [
65
+ "import os\n",
66
+ "import sys\n",
67
+ "from pathlib import Path\n",
68
+ "\n",
69
+ "# check if workding_dir is in local variables\n",
70
+ "if \"workding_dir\" not in locals():\n",
71
+ " workding_dir = str(Path.cwd().parent)\n",
72
+ "\n",
73
+ "os.chdir(workding_dir)\n",
74
+ "sys.path.append(workding_dir)\n",
75
+ "print(\"workding dir:\", workding_dir)"
76
+ ]
77
+ },
78
+ {
79
+ "cell_type": "code",
80
+ "execution_count": 3,
81
+ "metadata": {
82
+ "application/vnd.databricks.v1+cell": {
83
+ "cellMetadata": {
84
+ "byteLimit": 2048000,
85
+ "rowLimit": 10000
86
+ },
87
+ "inputWidgets": {},
88
+ "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775",
89
+ "showTitle": false,
90
+ "title": ""
91
+ },
92
+ "colab": {
93
+ "base_uri": "https://localhost:8080/"
94
+ },
95
+ "executionInfo": {
96
+ "elapsed": 3,
97
+ "status": "ok",
98
+ "timestamp": 1720679529345,
99
+ "user": {
100
+ "displayName": "HUANG DONGHAO _",
101
+ "userId": "00977795705617022768"
102
+ },
103
+ "user_tz": -480
104
+ },
105
+ "id": "hPCC-6m7ZBrM",
106
+ "outputId": "c7aa2c96-5e99-440a-c148-201d79465ff9"
107
+ },
108
+ "outputs": [
109
+ {
110
+ "name": "stdout",
111
+ "output_type": "stream",
112
+ "text": [
113
+ "loading env vars from: d:\\code\\projects\\rapget-translation\\.env\n"
114
+ ]
115
+ },
116
+ {
117
+ "data": {
118
+ "text/plain": [
119
+ "True"
120
+ ]
121
+ },
122
+ "execution_count": 3,
123
+ "metadata": {},
124
+ "output_type": "execute_result"
125
+ }
126
+ ],
127
+ "source": [
128
+ "from dotenv import find_dotenv, load_dotenv\n",
129
+ "\n",
130
+ "found_dotenv = find_dotenv(\".env\")\n",
131
+ "\n",
132
+ "if len(found_dotenv) == 0:\n",
133
+ " found_dotenv = find_dotenv(\".env.example\")\n",
134
+ "print(f\"loading env vars from: {found_dotenv}\")\n",
135
+ "load_dotenv(found_dotenv, override=True)"
136
+ ]
137
+ },
138
+ {
139
+ "cell_type": "code",
140
+ "execution_count": 4,
141
+ "metadata": {
142
+ "application/vnd.databricks.v1+cell": {
143
+ "cellMetadata": {
144
+ "byteLimit": 2048000,
145
+ "rowLimit": 10000
146
+ },
147
+ "inputWidgets": {},
148
+ "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc",
149
+ "showTitle": false,
150
+ "title": ""
151
+ },
152
+ "colab": {
153
+ "base_uri": "https://localhost:8080/"
154
+ },
155
+ "executionInfo": {
156
+ "elapsed": 3,
157
+ "status": "ok",
158
+ "timestamp": 1720679529345,
159
+ "user": {
160
+ "displayName": "HUANG DONGHAO _",
161
+ "userId": "00977795705617022768"
162
+ },
163
+ "user_tz": -480
164
+ },
165
+ "id": "1M3IraVtZBrM",
166
+ "outputId": "29ab35f6-2970-4ade-d85d-3174acf8cda0"
167
+ },
168
+ "outputs": [
169
+ {
170
+ "name": "stdout",
171
+ "output_type": "stream",
172
+ "text": [
173
+ "01-ai/Yi-1.5-9B-Chat None True datasets/mac/mac.tsv results/mac-results_few_shots_4bit.csv False 300\n"
174
+ ]
175
+ }
176
+ ],
177
+ "source": [
178
+ "import os\n",
179
+ "\n",
180
+ "model_name = os.getenv(\"MODEL_NAME\")\n",
181
+ "adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n",
182
+ "load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n",
183
+ "data_path = os.getenv(\"DATA_PATH\")\n",
184
+ "results_path = os.getenv(\"RESULTS_PATH\")\n",
185
+ "use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n",
186
+ "max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n",
187
+ "\n",
188
+ "print(\n",
189
+ " model_name,\n",
190
+ " adapter_name_or_path,\n",
191
+ " load_in_4bit,\n",
192
+ " data_path,\n",
193
+ " results_path,\n",
194
+ " use_english_datasets,\n",
195
+ " max_new_tokens,\n",
196
+ ")"
197
+ ]
198
+ },
199
+ {
200
+ "cell_type": "code",
201
+ "execution_count": 5,
202
+ "metadata": {
203
+ "application/vnd.databricks.v1+cell": {
204
+ "cellMetadata": {
205
+ "byteLimit": 2048000,
206
+ "rowLimit": 10000
207
+ },
208
+ "inputWidgets": {},
209
+ "nuid": "b2a43943-9324-4839-9a47-cfa72de2244b",
210
+ "showTitle": false,
211
+ "title": ""
212
+ },
213
+ "colab": {
214
+ "base_uri": "https://localhost:8080/"
215
+ },
216
+ "executionInfo": {
217
+ "elapsed": 564,
218
+ "status": "ok",
219
+ "timestamp": 1720679529907,
220
+ "user": {
221
+ "displayName": "HUANG DONGHAO _",
222
+ "userId": "00977795705617022768"
223
+ },
224
+ "user_tz": -480
225
+ },
226
+ "id": "UgMvt6dIZBrM",
227
+ "outputId": "ce37581c-fd26-46c2-ad87-d933d99f68f7"
228
+ },
229
+ "outputs": [
230
+ {
231
+ "name": "stdout",
232
+ "output_type": "stream",
233
+ "text": [
234
+ "Python 3.11.9\n",
235
+ "Name: torch\n",
236
+ "Version: 2.4.0+cu124\n",
237
+ "Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
238
+ "Home-page: https://pytorch.org/\n",
239
+ "Author: PyTorch Team\n",
240
+ "Author-email: packages@pytorch.org\n",
241
+ "License: BSD-3\n",
242
+ "Location: C:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\n",
243
+ "Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n",
244
+ "Required-by: accelerate, bitsandbytes, peft, torchaudio, torchvision\n",
245
+ "---\n",
246
+ "Name: transformers\n",
247
+ "Version: 4.43.3\n",
248
+ "Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n",
249
+ "Home-page: https://github.com/huggingface/transformers\n",
250
+ "Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n",
251
+ "Author-email: transformers@huggingface.co\n",
252
+ "License: Apache 2.0 License\n",
253
+ "Location: C:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\n",
254
+ "Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n",
255
+ "Required-by: peft\n",
256
+ "CPU times: total: 0 ns\n",
257
+ "Wall time: 8.35 s\n"
258
+ ]
259
+ }
260
+ ],
261
+ "source": [
262
+ "%%time\n",
263
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
264
+ "\n",
265
+ "!python --version\n",
266
+ "!pip show torch transformers"
267
+ ]
268
+ },
269
+ {
270
+ "cell_type": "code",
271
+ "execution_count": 6,
272
+ "metadata": {
273
+ "colab": {
274
+ "base_uri": "https://localhost:8080/"
275
+ },
276
+ "executionInfo": {
277
+ "elapsed": 1685,
278
+ "status": "ok",
279
+ "timestamp": 1720679531591,
280
+ "user": {
281
+ "displayName": "HUANG DONGHAO _",
282
+ "userId": "00977795705617022768"
283
+ },
284
+ "user_tz": -480
285
+ },
286
+ "id": "ZuS_FsLyZBrN",
287
+ "outputId": "2cba0105-c505-4395-afbd-2f2fee6581d0"
288
+ },
289
+ "outputs": [
290
+ {
291
+ "name": "stderr",
292
+ "output_type": "stream",
293
+ "text": [
294
+ "c:\\Users\\dongh\\.conda\\envs\\rapget\\Lib\\site-packages\\threadpoolctl.py:1214: RuntimeWarning: \n",
295
+ "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n",
296
+ "the same time. Both libraries are known to be incompatible and this\n",
297
+ "can cause random crashes or deadlocks on Linux when loaded in the\n",
298
+ "same Python program.\n",
299
+ "Using threadpoolctl may cause crashes or deadlocks. For more\n",
300
+ "information and possible workarounds, please see\n",
301
+ " https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n",
302
+ "\n",
303
+ " warnings.warn(msg, RuntimeWarning)\n",
304
+ "[nltk_data] Downloading package wordnet to\n",
305
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
306
+ "[nltk_data] Package wordnet is already up-to-date!\n",
307
+ "[nltk_data] Downloading package punkt to\n",
308
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
309
+ "[nltk_data] Package punkt is already up-to-date!\n",
310
+ "[nltk_data] Downloading package omw-1.4 to\n",
311
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
312
+ "[nltk_data] Package omw-1.4 is already up-to-date!\n"
313
+ ]
314
+ },
315
+ {
316
+ "name": "stdout",
317
+ "output_type": "stream",
318
+ "text": [
319
+ "loading: d:\\code\\projects\\rapget-translation\\eval_modules\\calc_repetitions.py\n",
320
+ "loading d:\\code\\projects\\rapget-translation\\llm_toolkit\\translation_utils.py\n"
321
+ ]
322
+ },
323
+ {
324
+ "name": "stderr",
325
+ "output_type": "stream",
326
+ "text": [
327
+ "[nltk_data] Downloading package wordnet to\n",
328
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
329
+ "[nltk_data] Package wordnet is already up-to-date!\n",
330
+ "[nltk_data] Downloading package punkt to\n",
331
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
332
+ "[nltk_data] Package punkt is already up-to-date!\n",
333
+ "[nltk_data] Downloading package omw-1.4 to\n",
334
+ "[nltk_data] C:\\Users\\dongh\\AppData\\Roaming\\nltk_data...\n",
335
+ "[nltk_data] Package omw-1.4 is already up-to-date!\n"
336
+ ]
337
+ },
338
+ {
339
+ "name": "stdout",
340
+ "output_type": "stream",
341
+ "text": [
342
+ "CUDA is available, we have found 1 GPU(s)\n",
343
+ "NVIDIA GeForce RTX 4080 Laptop GPU\n",
344
+ "CUDA version: 12.4\n"
345
+ ]
346
+ }
347
+ ],
348
+ "source": [
349
+ "from llm_toolkit.llm_utils import *\n",
350
+ "from llm_toolkit.translation_utils import *\n",
351
+ "\n",
352
+ "device = check_gpu()"
353
+ ]
354
+ },
355
+ {
356
+ "cell_type": "code",
357
+ "execution_count": 7,
358
+ "metadata": {},
359
+ "outputs": [
360
+ {
361
+ "name": "stdout",
362
+ "output_type": "stream",
363
+ "text": [
364
+ "loading train/test data files\n",
365
+ "DatasetDict({\n",
366
+ " train: Dataset({\n",
367
+ " features: ['chinese', 'english'],\n",
368
+ " num_rows: 4528\n",
369
+ " })\n",
370
+ " test: Dataset({\n",
371
+ " features: ['chinese', 'english'],\n",
372
+ " num_rows: 1133\n",
373
+ " })\n",
374
+ "})\n"
375
+ ]
376
+ }
377
+ ],
378
+ "source": [
379
+ "datasets = load_translation_dataset(data_path)"
380
+ ]
381
+ },
382
+ {
383
+ "cell_type": "code",
384
+ "execution_count": 8,
385
+ "metadata": {},
386
+ "outputs": [],
387
+ "source": [
388
+ "os.getenv(\"OPENAI_MODEL\")\n",
389
+ "base_url = os.getenv(\"OPENAI_BASE_URL\") or None"
390
+ ]
391
+ },
392
+ {
393
+ "cell_type": "code",
394
+ "execution_count": 9,
395
+ "metadata": {},
396
+ "outputs": [
397
+ {
398
+ "name": "stdout",
399
+ "output_type": "stream",
400
+ "text": [
401
+ "--------------------------------------------------\n",
402
+ "chinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\n",
403
+ "--------------------------------------------------\n",
404
+ "english: When Grannie Liu heard Xi-feng talk about 'difficulties' she concluded that there was no hope. Her delight and the way in which her face lit up with pleasure when she heard that she was, after all, to be given twenty taels of silver can be imagined. 'We knew you had your troubles,' she said, 'but as the saying goes, 'A starved camel is bigger than a fat horse.'\n",
405
+ "--------------------------------------------------\n",
406
+ "chinese: 后来她不挣扎了,对我说,混蛋,你要把我怎么办。\n",
407
+ "--------------------------------------------------\n",
408
+ "english: After a while, she no longer struggled and said, You bastard! What are you going to do with me?\n"
409
+ ]
410
+ }
411
+ ],
412
+ "source": [
413
+ "eval_dataset = datasets[\"test\"].select([260, 908])\n",
414
+ "print_row_details(eval_dataset.to_pandas(), range(len(eval_dataset)))"
415
+ ]
416
+ },
417
+ {
418
+ "cell_type": "code",
419
+ "execution_count": 10,
420
+ "metadata": {},
421
+ "outputs": [
422
+ {
423
+ "name": "stdout",
424
+ "output_type": "stream",
425
+ "text": [
426
+ "You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\n",
427
+ "\n",
428
+ "Example Translations:\n",
429
+ "Chinese: 全仗着狐仙搭救。\n",
430
+ "English: Because I was protected by a fox fairy.\n",
431
+ "Chinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\n",
432
+ "English: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\n",
433
+ "Chinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\n",
434
+ "English: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\n",
435
+ "Chinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\n",
436
+ "English: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\n",
437
+ "Chinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\n",
438
+ "English: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\n",
439
+ "\n",
440
+ "Chinese: {input}\n",
441
+ "English:\n"
442
+ ]
443
+ }
444
+ ],
445
+ "source": [
446
+ "translation_prompt = get_few_shot_prompt(datasets[\"train\"], num_shots=5)\n",
447
+ "print(translation_prompt)"
448
+ ]
449
+ },
450
+ {
451
+ "cell_type": "code",
452
+ "execution_count": 11,
453
+ "metadata": {},
454
+ "outputs": [
455
+ {
456
+ "name": "stdout",
457
+ "output_type": "stream",
458
+ "text": [
459
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n",
460
+ "\u001b[0m{\n",
461
+ " \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n",
462
+ "}\n",
463
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
464
+ "\u001b[0m{\n",
465
+ " \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n",
466
+ "}\n",
467
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
468
+ "\u001b[0m[outputs]\n",
469
+ "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n",
470
+ "\u001b[0m{\n",
471
+ " \"prompts\": [\n",
472
+ " \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\\nEnglish:\"\n",
473
+ " ]\n",
474
+ "}\n",
475
+ "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [1.45s] Exiting LLM run with output:\n",
476
+ "\u001b[0m{\n",
477
+ " \"generations\": [\n",
478
+ " [\n",
479
+ " {\n",
480
+ " \"text\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know about difficulties, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n",
481
+ " \"generation_info\": {\n",
482
+ " \"finish_reason\": \"stop\",\n",
483
+ " \"logprobs\": null\n",
484
+ " },\n",
485
+ " \"type\": \"ChatGeneration\",\n",
486
+ " \"message\": {\n",
487
+ " \"lc\": 1,\n",
488
+ " \"type\": \"constructor\",\n",
489
+ " \"id\": [\n",
490
+ " \"langchain\",\n",
491
+ " \"schema\",\n",
492
+ " \"messages\",\n",
493
+ " \"AIMessage\"\n",
494
+ " ],\n",
495
+ " \"kwargs\": {\n",
496
+ " \"content\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know about difficulties, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n",
497
+ " \"response_metadata\": {\n",
498
+ " \"token_usage\": {\n",
499
+ " \"completion_tokens\": 56,\n",
500
+ " \"prompt_tokens\": 484,\n",
501
+ " \"total_tokens\": 540\n",
502
+ " },\n",
503
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
504
+ " \"system_fingerprint\": \"fp_0f03d4f0ee\",\n",
505
+ " \"finish_reason\": \"stop\",\n",
506
+ " \"logprobs\": null\n",
507
+ " },\n",
508
+ " \"type\": \"ai\",\n",
509
+ " \"id\": \"run-1c7d8c24-e2d6-4ba3-b87a-16101fb1ce80-0\",\n",
510
+ " \"usage_metadata\": {\n",
511
+ " \"input_tokens\": 484,\n",
512
+ " \"output_tokens\": 56,\n",
513
+ " \"total_tokens\": 540\n",
514
+ " },\n",
515
+ " \"tool_calls\": [],\n",
516
+ " \"invalid_tool_calls\": []\n",
517
+ " }\n",
518
+ " }\n",
519
+ " }\n",
520
+ " ]\n",
521
+ " ],\n",
522
+ " \"llm_output\": {\n",
523
+ " \"token_usage\": {\n",
524
+ " \"completion_tokens\": 56,\n",
525
+ " \"prompt_tokens\": 484,\n",
526
+ " \"total_tokens\": 540\n",
527
+ " },\n",
528
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
529
+ " \"system_fingerprint\": \"fp_0f03d4f0ee\"\n",
530
+ " },\n",
531
+ " \"run\": null\n",
532
+ "}\n",
533
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [1.46s] Exiting Chain run with output:\n",
534
+ "\u001b[0m[outputs]\n"
535
+ ]
536
+ },
537
+ {
538
+ "data": {
539
+ "text/plain": [
540
+ "'That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \"We also know about difficulties, but as the saying goes: \\'A dead camel is still bigger than a horse.\\'\"'"
541
+ ]
542
+ },
543
+ "execution_count": 11,
544
+ "metadata": {},
545
+ "output_type": "execute_result"
546
+ }
547
+ ],
548
+ "source": [
549
+ "from langchain_core.globals import set_debug\n",
550
+ "\n",
551
+ "set_debug(True)\n",
552
+ "\n",
553
+ "translate_via_openai(\n",
554
+ " eval_dataset[\"chinese\"][0], translation_prompt, max_tokens=max_new_tokens\n",
555
+ ")"
556
+ ]
557
+ },
558
+ {
559
+ "cell_type": "code",
560
+ "execution_count": 12,
561
+ "metadata": {},
562
+ "outputs": [],
563
+ "source": [
564
+ "datasets[\"test\"] = eval_dataset"
565
+ ]
566
+ },
567
+ {
568
+ "cell_type": "code",
569
+ "execution_count": 13,
570
+ "metadata": {},
571
+ "outputs": [
572
+ {
573
+ "name": "stderr",
574
+ "output_type": "stream",
575
+ "text": [
576
+ " 0%| | 0/2 [00:00<?, ?it/s]"
577
+ ]
578
+ },
579
+ {
580
+ "name": "stdout",
581
+ "output_type": "stream",
582
+ "text": [
583
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n",
584
+ "\u001b[0m{\n",
585
+ " \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n",
586
+ "}\n",
587
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
588
+ "\u001b[0m{\n",
589
+ " \"input\": \"那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\"\n",
590
+ "}\n",
591
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
592
+ "\u001b[0m[outputs]\n",
593
+ "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n",
594
+ "\u001b[0m{\n",
595
+ " \"prompts\": [\n",
596
+ " \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 那刘姥姥先听见告艰苦,只当是没想头了, 又听见给他二十两银子,喜的眉开眼笑道:“我们也知道艰难的,但只俗语说的:‘瘦死的骆驼比马还大’呢。\\nEnglish:\"\n",
597
+ " ]\n",
598
+ "}\n"
599
+ ]
600
+ },
601
+ {
602
+ "name": "stderr",
603
+ "output_type": "stream",
604
+ "text": [
605
+ " 50%|█████ | 1/2 [00:02<00:02, 2.31s/it]"
606
+ ]
607
+ },
608
+ {
609
+ "name": "stdout",
610
+ "output_type": "stream",
611
+ "text": [
612
+ "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [1.27s] Exiting LLM run with output:\n",
613
+ "\u001b[0m{\n",
614
+ " \"generations\": [\n",
615
+ " [\n",
616
+ " {\n",
617
+ " \"text\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know it's difficult, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n",
618
+ " \"generation_info\": {\n",
619
+ " \"finish_reason\": \"stop\",\n",
620
+ " \"logprobs\": null\n",
621
+ " },\n",
622
+ " \"type\": \"ChatGeneration\",\n",
623
+ " \"message\": {\n",
624
+ " \"lc\": 1,\n",
625
+ " \"type\": \"constructor\",\n",
626
+ " \"id\": [\n",
627
+ " \"langchain\",\n",
628
+ " \"schema\",\n",
629
+ " \"messages\",\n",
630
+ " \"AIMessage\"\n",
631
+ " ],\n",
632
+ " \"kwargs\": {\n",
633
+ " \"content\": \"That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \\\"We also know it's difficult, but as the saying goes: 'A dead camel is still bigger than a horse.'\\\"\",\n",
634
+ " \"response_metadata\": {\n",
635
+ " \"token_usage\": {\n",
636
+ " \"completion_tokens\": 56,\n",
637
+ " \"prompt_tokens\": 484,\n",
638
+ " \"total_tokens\": 540\n",
639
+ " },\n",
640
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
641
+ " \"system_fingerprint\": \"fp_9b0abffe81\",\n",
642
+ " \"finish_reason\": \"stop\",\n",
643
+ " \"logprobs\": null\n",
644
+ " },\n",
645
+ " \"type\": \"ai\",\n",
646
+ " \"id\": \"run-68581936-c5f8-4d63-a40a-9ae04e88d234-0\",\n",
647
+ " \"usage_metadata\": {\n",
648
+ " \"input_tokens\": 484,\n",
649
+ " \"output_tokens\": 56,\n",
650
+ " \"total_tokens\": 540\n",
651
+ " },\n",
652
+ " \"tool_calls\": [],\n",
653
+ " \"invalid_tool_calls\": []\n",
654
+ " }\n",
655
+ " }\n",
656
+ " }\n",
657
+ " ]\n",
658
+ " ],\n",
659
+ " \"llm_output\": {\n",
660
+ " \"token_usage\": {\n",
661
+ " \"completion_tokens\": 56,\n",
662
+ " \"prompt_tokens\": 484,\n",
663
+ " \"total_tokens\": 540\n",
664
+ " },\n",
665
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
666
+ " \"system_fingerprint\": \"fp_9b0abffe81\"\n",
667
+ " },\n",
668
+ " \"run\": null\n",
669
+ "}\n",
670
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [1.28s] Exiting Chain run with output:\n",
671
+ "\u001b[0m[outputs]\n",
672
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n",
673
+ "\u001b[0m{\n",
674
+ " \"input\": \"后来她不挣扎了,对我说,混蛋,你要把我怎么办。\"\n",
675
+ "}\n",
676
+ "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
677
+ "\u001b[0m{\n",
678
+ " \"input\": \"后来她不挣扎了,对我说,混蛋,你要把我怎么办。\"\n",
679
+ "}\n",
680
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
681
+ "\u001b[0m[outputs]\n",
682
+ "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] Entering LLM run with input:\n",
683
+ "\u001b[0m{\n",
684
+ " \"prompts\": [\n",
685
+ " \"System: You are a helpful assistant that translates Chinese to English.\\nHuman: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.\\n\\nExample Translations:\\nChinese: 全仗着狐仙搭救。\\nEnglish: Because I was protected by a fox fairy.\\nChinese: 过后,表哥告诉她俩,这人是导演,在外国留过学的,还会编剧,今天拍的这戏,就是他自编自导的。\\nEnglish: He was the director, the cousin later told them. He had studied abroad and was also a screenwriter; in fact he had written and directed the scene they had earlier seen being filmed.\\nChinese: 这凤姐忽然想起一件事来,便向窗外叫:“蓉儿回来!”\\nEnglish: Xi-feng suddenly seemed to remember something, and called to him through the window, 'Rong, come back!'\\nChinese: 三个老红卫兵走到叶文洁面前,面对着她站成了一排——当年,她们也是这样面对叶哲泰的——试图再现那早已忘却的尊严,但她们当年那魔鬼般的精神力量显然已荡然无存。\\nEnglish: The three old Red Guards stood in front of Ye in a row—just like they had stood against Ye Zhetai—trying to recapture their long-forgotten dignity. But the demonic spiritual energy that had once propelled them was gone.\\nChinese: 程先生照单全收,都是一个“谢”字,然后问王琦瑶有什么话说。\\nEnglish: Mr. Cheng accepted their toast with equanimity and a 'thank you.' Then, turning to Wang Qiyao, he asked if she had anything to say.\\n\\nChinese: 后来她不挣扎了,对我说,混蛋,你要把我怎么办。\\nEnglish:\"\n",
686
+ " ]\n",
687
+ "}\n"
688
+ ]
689
+ },
690
+ {
691
+ "name": "stderr",
692
+ "output_type": "stream",
693
+ "text": [
694
+ "100%|██████████| 2/2 [00:04<00:00, 2.12s/it]"
695
+ ]
696
+ },
697
+ {
698
+ "name": "stdout",
699
+ "output_type": "stream",
700
+ "text": [
701
+ "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > llm:ChatOpenAI] [908ms] Exiting LLM run with output:\n",
702
+ "\u001b[0m{\n",
703
+ " \"generations\": [\n",
704
+ " [\n",
705
+ " {\n",
706
+ " \"text\": \"Later, she stopped struggling and said to me, \\\"Bastard, what are you going to do with me?\\\"\",\n",
707
+ " \"generation_info\": {\n",
708
+ " \"finish_reason\": \"stop\",\n",
709
+ " \"logprobs\": null\n",
710
+ " },\n",
711
+ " \"type\": \"ChatGeneration\",\n",
712
+ " \"message\": {\n",
713
+ " \"lc\": 1,\n",
714
+ " \"type\": \"constructor\",\n",
715
+ " \"id\": [\n",
716
+ " \"langchain\",\n",
717
+ " \"schema\",\n",
718
+ " \"messages\",\n",
719
+ " \"AIMessage\"\n",
720
+ " ],\n",
721
+ " \"kwargs\": {\n",
722
+ " \"content\": \"Later, she stopped struggling and said to me, \\\"Bastard, what are you going to do with me?\\\"\",\n",
723
+ " \"response_metadata\": {\n",
724
+ " \"token_usage\": {\n",
725
+ " \"completion_tokens\": 24,\n",
726
+ " \"prompt_tokens\": 433,\n",
727
+ " \"total_tokens\": 457\n",
728
+ " },\n",
729
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
730
+ " \"system_fingerprint\": \"fp_611b667b19\",\n",
731
+ " \"finish_reason\": \"stop\",\n",
732
+ " \"logprobs\": null\n",
733
+ " },\n",
734
+ " \"type\": \"ai\",\n",
735
+ " \"id\": \"run-e4bb10fb-f7c4-4440-82ba-c13a1a82bc00-0\",\n",
736
+ " \"usage_metadata\": {\n",
737
+ " \"input_tokens\": 433,\n",
738
+ " \"output_tokens\": 24,\n",
739
+ " \"total_tokens\": 457\n",
740
+ " },\n",
741
+ " \"tool_calls\": [],\n",
742
+ " \"invalid_tool_calls\": []\n",
743
+ " }\n",
744
+ " }\n",
745
+ " }\n",
746
+ " ]\n",
747
+ " ],\n",
748
+ " \"llm_output\": {\n",
749
+ " \"token_usage\": {\n",
750
+ " \"completion_tokens\": 24,\n",
751
+ " \"prompt_tokens\": 433,\n",
752
+ " \"total_tokens\": 457\n",
753
+ " },\n",
754
+ " \"model_name\": \"gpt-4o-mini-2024-07-18\",\n",
755
+ " \"system_fingerprint\": \"fp_611b667b19\"\n",
756
+ " },\n",
757
+ " \"run\": null\n",
758
+ "}\n",
759
+ "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [918ms] Exiting Chain run with output:\n",
760
+ "\u001b[0m[outputs]\n"
761
+ ]
762
+ },
763
+ {
764
+ "name": "stderr",
765
+ "output_type": "stream",
766
+ "text": [
767
+ "\n"
768
+ ]
769
+ }
770
+ ],
771
+ "source": [
772
+ "predictions = eval_openai(5, datasets)"
773
+ ]
774
+ },
775
+ {
776
+ "cell_type": "code",
777
+ "execution_count": 14,
778
+ "metadata": {},
779
+ "outputs": [
780
+ {
781
+ "name": "stdout",
782
+ "output_type": "stream",
783
+ "text": [
784
+ "['That Liu Laolao first heard about the hardships and thought it was hopeless, then heard about the twenty taels of silver and smiled with joy, saying, \"We also know it\\'s difficult, but as the saying goes: \\'A dead camel is still bigger than a horse.\\'\"', 'Later, she stopped struggling and said to me, \"Bastard, what are you going to do with me?\"']\n"
785
+ ]
786
+ }
787
+ ],
788
+ "source": [
789
+ "print(predictions)"
790
+ ]
791
+ },
792
+ {
793
+ "cell_type": "code",
794
+ "execution_count": 15,
795
+ "metadata": {},
796
+ "outputs": [
797
+ {
798
+ "data": {
799
+ "text/plain": [
800
+ "{'meteor': 0.5376810911615811,\n",
801
+ " 'bleu_scores': {'bleu': 0.16133991724232039,\n",
802
+ " 'precisions': [0.5454545454545454,\n",
803
+ " 0.26666666666666666,\n",
804
+ " 0.1643835616438356,\n",
805
+ " 0.09859154929577464],\n",
806
+ " 'brevity_penalty': 0.7322097138745853,\n",
807
+ " 'length_ratio': 0.7623762376237624,\n",
808
+ " 'translation_length': 77,\n",
809
+ " 'reference_length': 101},\n",
810
+ " 'rouge_scores': {'rouge1': 0.5594202898550725,\n",
811
+ " 'rouge2': 0.362051015096304,\n",
812
+ " 'rougeL': 0.5246376811594203,\n",
813
+ " 'rougeLsum': 0.5246376811594203},\n",
814
+ " 'accuracy': 0.0}"
815
+ ]
816
+ },
817
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3
+ Qwen/Qwen2-72B-Instruct,1.02,0.7573581492858762,0.4796836675632054,19.0137490702917,0.190137490702917,0.4520458023287854,0.0,0.08120035304501325,0.08120035304501325,0.7547074311557754,1.0,0
4
+ Qwen/Qwen2-72B-Instruct,1.04,0.7574983964372501,0.476362681282195,18.52063321160408,0.1852063321160408,0.4489040568717591,0.0,0.10503089143865843,0.10503089143865843,0.7540766638515739,1.0,0
5
+ Qwen/Qwen2-72B-Instruct,1.06,0.7563851308422812,0.4679636383950649,17.982473951038504,0.1798247395103851,0.441062203630729,0.0,0.07325684024713151,0.07325684024713151,0.7539950363948406,1.0,0
6
+ Qwen/Qwen2-72B-Instruct,1.08,0.7554470169417962,0.4597578197711645,17.067954025424825,0.1706795402542483,0.4317496244624469,0.0,0.04766107678729038,0.04766107678729038,0.753890250753016,1.0,0
7
+ Qwen/Qwen2-72B-Instruct,1.10,0.7550096405046973,0.4515778511124262,16.22452191616505,0.1622452191616504,0.4242083184666598,0.0,0.05736981465136805,0.05736981465136805,0.7531385283434515,1.0,0
8
  Qwen/Qwen2-7B-Instruct,1.00,0.7457721890965501,0.442240791493943,14.38814929350883,0.1438814929350883,0.4160546438584113,0.0,12.81288614298323,12.81288614298323,0.5490966301804292,0.9947043248014121,2
9
  Qwen/Qwen2-7B-Instruct,1.02,0.7474080664545482,0.4400998640836595,15.16172261831792,0.1516172261831792,0.4160749445105633,0.0,7.1562224183583405,7.1562224183583405,0.605472251398711,0.9947043248014121,1
10
+ Qwen/Qwen2-7B-Instruct,1.04,0.7484375637974869,0.4390136558190875,14.958631815014014,0.1495863181501401,0.4138111632957468,0.0,0.1853486319505737,0.1853486319505737,0.742515336500048,0.999117387466902,0
11
+ Qwen/Qwen2-7B-Instruct,1.06,0.7471612840233287,0.4328321576515084,14.28087386760537,0.1428087386760537,0.4069074178015074,0.0,0.2030008826125331,0.2030008826125331,0.7406965407448669,0.9982347749338041,0
12
+ Qwen/Qwen2-7B-Instruct,1.08,0.74519401609845,0.423560805217557,13.659683698817108,0.1365968369881711,0.3966315425573522,0.0,0.22153574580759047,0.22153574580759047,0.7381694832783203,1.0,0
13
+ Qwen/Qwen2-7B-Instruct,1.10,0.7432071187016402,0.4135053136541433,12.922649874705083,0.1292264987470507,0.3876193185383525,0.0,0.17740511915269197,0.17740511915269197,0.737574218462684,1.0,0
14
  internlm/internlm2_5-7b-chat,1.00,0.739699612254078,0.4289996929258777,14.734881589173108,0.1473488158917311,0.4096466800937898,0.0,12.751103265666373,12.751103265666373,0.5450982095784719,1.0,2
15
+ internlm/internlm2_5-7b-chat,1.02,0.740223803961056,0.4266246904302194,14.583816688798017,0.1458381668879802,0.4071727106228415,0.0,9.824360105913504,9.824360105913504,0.5706323412193933,1.0,1
16
+ internlm/internlm2_5-7b-chat,1.04,0.7398856264610577,0.4154585167056314,13.534659133050225,0.1353465913305021,0.3968657713589718,0.0,6.527802294792586,6.527802294792586,0.6073521998554903,1.0,1
17
+ internlm/internlm2_5-7b-chat,1.06,0.7379362287241489,0.4039588647855378,12.346740971499404,0.1234674097149939,0.3872447044295494,0.0,6.533980582524272,6.533980582524272,0.6056712925221626,0.999117387466902,1
18
+ internlm/internlm2_5-7b-chat,1.08,0.7319988705684732,0.3873176839854818,11.075674965706344,0.1107567496570634,0.3724352909668609,0.0,9.820829655781113,9.820829655781113,0.5643254582029298,0.999117387466902,1
19
+ internlm/internlm2_5-7b-chat,1.10,0.7295350462119345,0.3769306874386757,10.305163787094209,0.1030516378709421,0.3634496155759507,0.0,6.525154457193292,6.525154457193292,0.5988898943353679,0.999117387466902,1
20
+ microsoft/Phi-3.5-mini-instruct,1.00,0.7107840433177544,0.3796831545348129,8.71296896471494,0.0871296896471493,0.3589874395901284,0.0,28.42630185348632,28.42630185348632,0.4485492990665038,1.0,6
21
+ microsoft/Phi-3.5-mini-instruct,1.02,0.7164765837070485,0.3780585837553919,10.291240080163629,0.1029124008016362,0.3546952732427276,0.0,10.696381288614297,10.696381288614297,0.5444787777612768,1.0,2
22
+ microsoft/Phi-3.5-mini-instruct,1.04,0.7111233387336411,0.3547161333845742,8.966881655527896,0.0896688165552789,0.3300979657678754,0.0,3.7484554280670785,3.7484554280670785,0.6247493045810047,1.0,1
23
+ microsoft/Phi-3.5-mini-instruct,1.06,0.7024363270136286,0.3298733737040869,7.076233088011138,0.0707623308801113,0.3019513312669543,0.0,0.10767872903795234,0.10767872903795234,0.6991841213752065,1.0,0
24
+ microsoft/Phi-3.5-mini-instruct,1.08,0.6882111219210848,0.3054541022592767,5.105510599247868,0.0510551059924786,0.2736030007297014,0.0,3.29567519858782,3.29567519858782,0.6124452738619192,1.0,1
25
+ microsoft/Phi-3.5-mini-instruct,1.10,0.6712992989638161,0.2903831801547132,4.091958857999118,0.0409195885799911,0.251653275009876,0.0,0.07766990291262135,0.07766990291262135,0.6690512007506374,1.0,0
26
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.00,0.7501818982248062,0.4611110508507017,17.87914973742753,0.1787914973742752,0.4340662057009564,0.0,0.088261253309797,0.088261253309797,0.747329847981863,1.0,0
27
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.02,0.7485114382045625,0.4571517219079576,17.436884594979905,0.174368845949799,0.4311385932640979,0.0,0.09267431597528684,0.09267431597528684,0.7455246732136059,1.0,0
28
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.04,0.7500591586357918,0.4560467960364254,17.440173470996626,0.1744017347099662,0.4302844557731285,0.0,0.13062665489849956,0.13062665489849956,0.7458552866168927,1.0,0
29
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.06,0.748812871571673,0.4520416361219855,16.89523258317781,0.168952325831778,0.4260026774745837,0.0,0.12533097969991175,0.12533097969991175,0.7447841638756355,1.0,0
30
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.08,0.7473851635144647,0.4442106511292453,16.16623784482793,0.1616623784482792,0.4195129470585874,0.0,0.18711385701676964,0.18711385701676964,0.7414159053529263,1.0,0
31
+ shenzhi-wang/Llama3.1-70B-Chinese-Chat,1.10,0.7465709781131172,0.4379837926138161,15.60172257624066,0.1560172257624066,0.4132562932940978,0.0,0.08649602824360106,0.08649602824360106,0.743788967986371,1.0,0
32
+ shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.00,0.7426396049131678,0.433632501662176,15.209540658023398,0.1520954065802339,0.4089208235151474,0.0,5.798764342453663,5.798764342453663,0.619577239776096,1.0,1
33
  shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.02,0.7436477056353469,0.4329054166518245,15.19102241646024,0.1519102241646024,0.4068967964789407,0.0,5.77846425419241,5.77846425419241,0.6207074516423631,1.0,1
34
+ shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.04,0.7440943776351209,0.4320478700956207,15.05135166158296,0.1505135166158296,0.4062008380201262,0.0,0.11827007943512798,0.11827007943512798,0.7403141356055205,1.0,0
35
+ shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.06,0.7426502735395928,0.4275429314912545,14.449130821290163,0.1444913082129016,0.4001409979222783,0.0,0.176522506619594,0.176522506619594,0.7370491441666694,1.0,0
36
+ shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.08,0.7408098006080129,0.4206626658729054,13.933703757385222,0.1393370375738522,0.3964824268676203,0.0,0.21888790820829657,0.21888790820829657,0.7339083936807739,1.0,0
37
+ shenzhi-wang/Llama3.1-8B-Chinese-Chat,1.10,0.7392685912871718,0.4111211240399151,13.303738403756984,0.1330373840375698,0.3870959581563503,0.0,0.13857016769638128,0.13857016769638128,0.7348764469929082,1.0,0
38
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.00,0.7240239171358935,0.4068335357738006,13.565136550617618,0.1356513655061761,0.3866395067055498,0.0,0.1059135039717564,0.1059135039717564,0.7207261791424407,1.0,0
39
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.02,0.7263097057327799,0.4064914781094827,13.42987641622816,0.1342987641622816,0.3863697821025159,0.0,6.238305383936452,6.238305383936452,0.5999878425713037,1.0,1
40
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.04,0.7276128307708258,0.4054859896994975,13.295092218891954,0.1329509221889195,0.3851203729935697,0.0,0.1297440423654016,0.1297440423654016,0.7235619893938705,1.0,0
41
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.06,0.7276865132383193,0.4014727027723293,13.10860799057166,0.1310860799057166,0.3804952786306688,0.0,0.20741394527802295,0.20741394527802295,0.7212559915451495,1.0,0
42
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.08,0.726393195584298,0.3987018836449559,12.850537785783194,0.1285053778578319,0.3788945955746495,0.0,0.2903795233892321,0.2903795233892321,0.717473994791502,1.0,0
43
+ shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat,1.10,0.7244012304511832,0.3932239948456176,12.361161644811926,0.1236116164481192,0.3733413807007665,0.0,0.1500441306266549,0.1500441306266549,0.7197459635880831,1.0,0
scripts/eval-h100.sh CHANGED
@@ -57,14 +57,15 @@ export RESULTS_PATH=results/mac-results_rpp_with_mnt_2048_generic_prompt.csv
57
  export BATCH_SIZE=1
58
  export LOAD_IN_4BIT=true
59
 
 
60
  ./scripts/eval-rpp.sh Qwen Qwen2-72B-Instruct checkpoint-105
61
 
62
 
63
- export BATCH_SIZE=4
64
- export LOAD_IN_4BIT=false
65
 
66
- ./scripts/eval-rpp.sh shenzhi-wang Mistral-7B-v0.3-Chinese-Chat checkpoint-70
67
 
68
- ./scripts/eval-rpp.sh shenzhi-wang Llama3.1-8B-Chinese-Chat checkpoint-105
69
 
70
- ./scripts/eval-rpp.sh microsoft Phi-3.5-mini-instruct checkpoint-210
 
57
  export BATCH_SIZE=1
58
  export LOAD_IN_4BIT=true
59
 
60
+ export START_REPETITION_PENALTY=1.02
61
  ./scripts/eval-rpp.sh Qwen Qwen2-72B-Instruct checkpoint-105
62
 
63
 
64
+ # export BATCH_SIZE=4
65
+ # export LOAD_IN_4BIT=false
66
 
67
+ # ./scripts/eval-rpp.sh shenzhi-wang Mistral-7B-v0.3-Chinese-Chat checkpoint-70
68
 
69
+ # ./scripts/eval-rpp.sh shenzhi-wang Llama3.1-8B-Chinese-Chat checkpoint-105
70
 
71
+ # ./scripts/eval-rpp.sh microsoft Phi-3.5-mini-instruct checkpoint-210
scripts/eval-mac.sh CHANGED
@@ -54,10 +54,10 @@ export RESULTS_PATH=results/mac-results_rpp_with_mnt_2048_generic_prompt.csv
54
 
55
  #./scripts/eval-rpp.sh Qwen Qwen2-7B-Instruct checkpoint-105
56
 
57
- cd ../rapget-v2; pip install -r requirements.txt; python eval_modules/calc_bert_scores.py; cd -
58
 
59
- ./scripts/eval-rpp.sh shenzhi-wang Mistral-7B-v0.3-Chinese-Chat checkpoint-70
60
 
 
61
  ./scripts/eval-rpp.sh shenzhi-wang Llama3.1-8B-Chinese-Chat checkpoint-105
62
 
63
- ./scripts/eval-rpp.sh microsoft Phi-3.5-mini-instruct checkpoint-210
 
54
 
55
  #./scripts/eval-rpp.sh Qwen Qwen2-7B-Instruct checkpoint-105
56
 
57
+ # ./scripts/eval-rpp.sh shenzhi-wang Mistral-7B-v0.3-Chinese-Chat checkpoint-70
58
 
59
+ # ./scripts/eval-rpp.sh microsoft Phi-3.5-mini-instruct checkpoint-210
60
 
61
+ export BATCH_SIZE=1
62
  ./scripts/eval-rpp.sh shenzhi-wang Llama3.1-8B-Chinese-Chat checkpoint-105
63