# äœè
ã¯Google Colabç°å¢ã§å®è¡
# ãããã ãã¯ç°å¢ã«åãããŠèšå®ããŠãã ãããelyza-tasks-100-TV_0.jsonl ãšçµæã®ä¿åå Žæãæå®ããã
inputfile = "/data/data/elyza-tasks-100-TV_0.jsonl"
savefile = "/data/data/result.jsonl"
# # Google Colab ã®å Žåã¯äžèšã®ç°å¢æ§ç¯æé ãè¡ãªãããåã«ãã®ã»ã«ããå®è¡ããŠãã£ãŠãã ããã
!pip uninstall unsloth -y
!pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --upgrade torch
!pip install --upgrade xformers
import pandas as pd
import numpy as np
from difflib import SequenceMatcher
from unsloth import FastLanguageModel
import torch
model_id = "fathom-llm/checkpoint-2000"
dtype = None # Noneã«ããŠããã°èªåã§èšå®
load_in_4bit = True # ä»åã¯8Bã¯ã©ã¹ã®ã¢ãã«ãæ±ãããTrue
# # FastLanguageModel ã€ã³ã¹ã¿ã³ã¹ãäœæ
model, tokenizer = FastLanguageModel.from_pretrained(
model_id,
dtype=dtype,
max_seq_length = 1024,
load_in_4bit=load_in_4bit,
trust_remote_code=True,
)
# å
ã®ã¿ã¹ã¯ã«å¯Ÿå¿ããããã³ãã(å
ã®ã¿ã¹ã¯ãšã®é¡äŒŒåºŠã«ãã£ãŠããã³ãããèšå®)
instruction_data = [
{'input': 'ä»äºã®ç±æãåãæ»ãããã®ã¢ã€ãã¢ã5ã€æããŠãã ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãç¹å®ã®ç¶æ³ã課é¡ã«å¯ŸããŠåœ¹ç«ã€ã¢ã€ãã¢ãæããŠãã ãããææ¡ããã¢ã€ãã¢ã¯å®çšçã§ãå
·äœçãã€å€æ§æ§ã®ãããã®ã«ããŠãã ããããŸããããããã®ã¢ã€ãã¢ãã©ã®ããã«åœ¹ç«ã€ã®ãç°¡æœã«èª¬æããŠãã ããã'} ,
{'input': 'ã¯ãã海蟺ã«è¡ã£ãŠã¢ã¶ã©ã·ãšåéã«ãªããæçµçã«ã¯å®¶ã«åž°ããšããããããã®çç·šå°èª¬ãæžããŠãã ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæå®ãããããŒããããããã«åºã¥ããŠçç·šå°èª¬ãäœæããŠãã ãããç©èªã®æµããèªç¶ã§ãç»å Žäººç©ãèå°ãé
åçã«æãããããã«å¿æããŠãã ããããŸããããããã®éèŠãªèŠçŽ ãæŒããªãçã蟌ã¿ãèªè
ãåŒã蟌ãç©èªãå®æãããŠãã ããã'} ,
{'input': 'å€ä»£ã®ãªã·ã£ãåŠã¶äžã§ç¥ã£ãŠããã¹ããã€ã³ãã¯ïŒ', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžããããããŒãã«ã€ããŠéèŠãªãã€ã³ããããã€ãæãããããããç°¡æœãã€å
·äœçã§ãããããã説æããŠãã ããã'} ,
{'input': '以äžã®èª¬æãèªãã ãŠãŒã¶ãŒãæããã§ãããçåãšã¯ïŒ\r\n\r\n倧éã®ãŽãã®åŠçã«ãå°ãã§ã¯ãããŸãããïŒ\r\n\r\nãçç£ã§åºããŽãåŠçè²»çšã倧å¹
ã«ããã£ãŠããã\r\nãæå®ãŽãè¢ãå°å
¥ãããäžæ°ã«ã³ã¹ããäžãã£ãŠããŸã£ãã\r\nãå·¥å Žãçãã®ã§ããŽãä¿ç®¡ã¹ããŒã¹ãæžããããã\r\nãISO14001ã§ãŽãåæžã倧ããªèª²é¡ãšãªã£ãŠããã\r\n\r\nå Žæããšã嵩匵ããŽããã€ã¶ããŠãŸãšããŠéã³ããã圢ã«ããããšã§ãåŠçã»ä¿ç®¡ã»éæ¬ã«ãããæ§ã
ãªã³ã¹ããåæžããããç°å¢è² è·ã®äœæžã«ãè²¢ç®ã§ããã®ããå»æ£ç©å§çž®æžå®¹æ©âããŽãå§çž®æ©ãã§ãã\r\n\r\nãŽãå§çž®æ©ã®äž»ãªçšéã¯å»ãã©ã¹ããã¯ã»çŽã»æ®µããŒã«ã»è¢é¡ã»ãããããã«ã»ãã©ã¹ããã¯å®¹åšãªã©ã€ã¶ãããå
ã«æ»ã£ãŠããŸããŽããå§çž®æ¢±å
ããŸãã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæ瀺ããã説ææãèªãã ãŠãŒã¶ãŒãæããçåãæ³å®ããŠãã ããã説ææã®å
容ãäžè¶³ããŠããæ
å ±ãããšã«èãããŸãšããŠãã ããã'} ,
{'input': '次ã«èªãã¹ãSFã10åæããŠ', 'instruction': '以äžã®ã¿ã¹ã¯ã«å®åšããå
·äœäŸãããšã«åçããŠãã ãããåçã¯å¿
èŠã«å¿ããŠãªã¹ã圢åŒã§æŽçããããããã®é
ç®ã«çã説æãçç±ãæ·»ããŠãã ãããã¿ã¹ã¯ãç¹å®ã®ãžã£ã³ã«ã«é¢ãããã®ã§ããã°ããã®ãžã£ã³ã«ã®ç¹åŸŽãèæ
®ããåçãå¿ãããŠãã ããã'} ,
{'input': '次ã®æç« ãèªãã§ããã®äººãã©ã®çšåºŠæã£ãŠãããã1?10ã®å°ºåºŠã§è©äŸ¡ããŠãã ããã(1ïŒæã£ãŠããªãã10ïŒéåžžã«æã£ãŠãã)ã\r\n\r\n1. ãŸããã¹ãã§èµ€ç¹ããåã¯å
šã...\r\n2. ãã¹ãã§èµ€ç¹ïŒä»åã¯é£ããã£ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžããããå
容ãè©äŸ¡ããããã®å
·äœçãªåºæºãæ瀺ããŠãã ãããåçã¯ãæå®ããã尺床ã圢åŒã«åºã¥ããŠè¡ããå¯èœã§ããã°è©äŸ¡ã®çç±ãèæ¯ãç°¡æœã«è£è¶³ããŠãã ãããè©äŸ¡ã®éã¯ãæç« ãæèã«èŸŒããããææ
ãæå³ãç確ã«è§£éããŠãã ããã'} ,
{'input': 'ããããŠç€ŸäŒã®ç§©åºã¯æ¿æ²»å®¶ãæåž«ãèŠå®ã«ããç©ççãªåŒ·å¶ã®ã»ããã¡ã³ããŒåã
人ã®èªçºçãªæå¿ã«ãæ¯ããããŠããŸããå®éãåŠæ ¡ã®ææ¥ãèªç¿ã«ãªã£ããšããã誰ãããªãå Žæã§ä¿¡å·åŸ
ã¡ãããŠãããšãã誰ãèŠãŠããªãé端ã§ã¿ãã³ãæšãŠãããªã£ããšããªã©ãæ宀ã§å€§éšããããå ã
ãšã¿ãã³ãæãæšãŠãããšãå¯èœã§ããã«ãããããããããããããšã«ãªãããã®èºèºãæãã人ã倧åã ãšæããŸããïŒ<a>åè¥ç¡äººã«ãµããŸã人ãããŸãããïŒ\r\n\r\näžèšã®æç« ãèªãã§ã<a> ã«å
¥ããã®ã«æãé©ãããã®ã以äžã®éžæè¢ããéžã³ããã®çç±ãçããªããã\r\n\r\n- ã ãã\r\n- ãŸã\r\n- ããã\r\n- ãã¡ãã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæ瀺ãããæç« ãæèãåæããæå®ããã圢åŒã«åºã¥ããŠåçããŠãã ãããéžæè¢ããæé©ãªçããéžã³ããã®éžæãé©åã§ããçç±ãç°¡æœãã€æ確ã«èª¬æããŠãã ãããåçã®éã«ã¯ãæç« å
šäœã®æµããæå³ãèžãŸããéžæè¢ããšã®ãã¥ã¢ã³ã¹ãé©åæ§ãèæ
®ããŠãã ããã'} ,
{'input': '以äžã®è¡šããããã¹ãŠã®ç§ç®åãæœåºããŠãã ããã\r\n| ç§ç®åïœè¬åž«åïœæ宀å\r\n| 埮ç©ååŠ101ïœç°äžææïœ202å·å®€\r\n| çŸè¡å²ïœå±±ç°ïŒä»£çïŒïœããŒã«A', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžãããããªã¹ããæç« ããç¹å®ã®æ
å ±ãæœåºããŠãã ããã該åœãããã®ã ããæ£ç¢ºã«åãåºããŠç®æ¡æžãã§èšè¿°ããŠãã ããã'} ,
{'input': '以äžã®èšäºããæ¥ä»ããã¹ãŠæœåºããã\r\nããããä»å¬äžçªã®å¯æ³¢ â10幎ã«äžåºŠã®äœæž©ã«â 倧éªã«åãã\r\n\r\nãã®å¬äžçªã®éåžžã«åŒ·ãå¯æ°ãæµã蟌ãããã24æ¥ããåæ¥æ¬ãã西æ¥æ¬ã®æ¥æ¬æµ·åŽã§å€§éªãçå¹éªãšãªãã»ãããµã ãéªã®å°ãªã倪平æŽåŽã®å¹³å°ã§ã倧éªãšãªããããããããŸãã\r\nå
šåœçã«10幎ã«äžåºŠçšåºŠã®äœãæ°æž©ã«ãªãèŠèŸŒã¿ã§ãè·¯é¢ãæ°Žé管ã®åçµã«ãã被害ãåºããããããããŸããäºå®ã®å€æŽãæ€èšãããªã©ã倧éªãäœæž©ãžã®åããé²ããŠãã ããã\r\n\r\næ°è±¡åºã«ãããŸããšãæ¥æ¬ä»è¿ã¯24æ¥ãã26æ¥ æšææ¥ããã«ãããŠå¬åã®æ°å§é
眮ã匷ãŸããåæ¥æ¬ãã西æ¥æ¬ã®äžç©ºã«ã¯ããã®å¬äžçªã®éåžžã«åŒ·ãå¯æ°ãæµã蟌ãèŠèŸŒã¿ã§ãã\r\n\r\nãã®ãããåæ¥æ¬ãã西æ¥æ¬ã®æ¥æ¬æµ·åŽãäžå¿ã«å€§éªãçå¹éªãšãªããçæéã§ç©éªãæ¥æ¿ã«å¢ãããããããããŸãã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæ瀺ãããæç« ãããŒã¿ããç¹å®ã®æ
å ±ãæãåºããŠãã ãããæœåºå¯Ÿè±¡ã®æ
å ±ãæ£ç¢ºã«ãã¹ãŠãªã¹ãã¢ããããäœèšãªæ
å ±ã¯å«ããªãã§ãã ãããä»ã®é¡é¡ã«ã察å¿ã§ããããã«ãæå®ããã圢åŒãæ¡ä»¶ã«åºã¥ããŠçããããšãå¿ãããŠãã ããã'} ,
{'input': '以äžã®æ ç»ã¿ã€ãã«ã®ãªã¹ããäžãããããšããã¿ã€ãã«ã«å«ãŸããéœåžåããã¹ãŠæžãåºããªããã\r\nã»ãã³ã¯ãŒããŒããã®ææŠ\r\nã»ãã³ãã³ã®å€¢\r\nã»ç¹ç°ä¿¡é·ã®ç涯', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžãããããªã¹ããæç« ããç¹å®ã®æ
å ±ãæœåºããŠãã ããã該åœãããã®ã ããæ£ç¢ºã«åãåºããŠç®æ¡æžãã§èšè¿°ããŠãã ããã'} ,
{'input': '以äžã®è£œåã«ã€ããŠãFacebookã§æµãåºåãæžããŠãã ãããåäŸã®ãã芪ãã¿ãŒã²ããã«ããŠããã£ãããŒã«ããŠãã ããã\r\nã補åæŠèŠã\r\nåäŸã®ããã®GPSãã©ãã«ãŒãé³å£°é話ãã§ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæŠèŠã«åºã¥ããå
容ãçã蟌ããŠãæå®ããã補åããµãŒãã¹ã宣äŒããããã®5è¡çšåºŠã®ã¡ãã»ãŒãžãäœæããŠãã ãããã¿ãŒã²ããå±€ã«å¿ããè¡šçŸã䜿çšãããã£ãããŒã§èå³ãåŒãå
容ã«ä»äžããŠãã ããã'} ,
{'input': 'ãã®ãããã¯ã質åã«ç®èãã£ã·ãã«çããŠããããã£ãããããã§ãã\r\nãŠãŒã¶ãŒ: 1ããã°ã©ã ã¯äœãã³ãã§ããïŒ\r\nããã: ãŸãããã§ããïŒ 1ããã°ã©ã ã¯2.2ãã³ãã§ããããã¡ã¢ããŠãããŠãã ãããã\r\nãŠãŒã¶ãŒ: HTMLã£ãŠäœã®ç¥ã§ããïŒ\r\nããã:', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ããããŸã§ã®ãã£ãããããã®ããŒã³ãåæ ããçãè¿çãããŠãã ããã'} ,
{'input': 'IMEãšããŠæ©èœããŠãã ããã\r\n\r\nããªãã®ã¿ã¹ã¯ã¯ããŒãåå
¥åãããã¢ã«ãã¡ãããåãåèªããšã«ã²ãããªã«å€æããããããã®åèªã挢åã«å€æããåè£ãæ倧3ã€æ瀺ããããšã§ãã\r\n\r\n以äžã«äŸã瀺ããŸãã\r\n\r\n```\r\ninputs:\r\nk, y, o, u, h, a, a, m, e, p, p, o, i\r\n\r\noutputs:\r\n- ãããã¯: ä»æ¥ã¯, æ掟, 京ã¯\r\n- ãã: éš, 风, ã¢ã¡\r\n- ã£ãœã: ã£ãœã, ããã€\r\n```\r\n\r\nããã§ã¯ãå
¥åãéå§ããã®ã§ãIMEãšããŠåºåã衚瀺ããŠãã ããã\r\n\r\ninputs:\r\nj, u, u, b, u, n, o, s, h, o, s, e, k, i', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãããŒãåãã²ãããªãžå€æããåŸãåèªãžåå²ããŠãæ瀺ããããã©ãŒãããã«åŸã£ãŠæŒ¢åå€æåè£ã®æ瀺ããã¿ã¹ã¯ã§ããåºåã«ã¯ãå¿
èŠã«å¿ããŠè€æ°ã®åè£ãæäŸããæå®ããã圢åŒãã«ãŒã«ã«åŸãããã«æ³šæããŠãã ããã'} ,
{'input': 'ããã¯æããéããããããšã®äŒè©±ã§ããç¥æµãšæã«æºã¡ãŠããäŒè©±ãããŠãã ããã\r\nç§: ã©ããããããã倧ããªå¹³åãšå¹³ç©ãåŸãããšãã§ããŸããïŒ\r\nããã:', 'instruction': '次ã®ã¿ã¹ã¯ã§ã¯ããã£ã©ã¯ã¿ãŒã®èšå®ãšäŒè©±çžæã®è³ªåãæ瀺ãããŸããäžããããäŒè©±ã®åãã«ãã£ã©ã¯ã¿ãŒã®ç¥ç§çãªè¿çãæäŸããŠãã ããã'} ,
{'input': '次ã®ã¹ããŒãªãŒã«ã€ããŠã®è³ªåã«çããŠãã ããã\r\nããæ¥ã6人ã®ç²äººã象ã觊ã£ãŠãã®æ£äœãçªããšããããšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®éŒ»ã«è§Šããã象ãšã¯èã®ãããªãã®ã ããšèšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®è³ã«è§Šããã象ãšã¯ãã¡ãã®ãããªãã®ã ããšèšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®è¶³ã«è§Šããã象ãšã¯æšã®å¹¹ã®ãããªãã®ã ããšèšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®èŽäœã«è§Šããã象ãšã¯å£ã®ãããªãã®ã ããšèšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®ãã£ãœã«è§Šãã象ãšã¯ããŒãã®ãããªãã®ã ããšèšããŸããã\r\n\r\nïŒäººç®ã¯è±¡ã®çã«è§Šããã象ãšã¯æ§ã®ãããªãã®ã ããšèšããŸããã\r\n\r\nããããïŒäººã®ç²äººãã¡ã¯é·ãããšèšãäºãããããããèªåã®æèŠãè²ããŸããã§ããã\r\n\r\nQ: ãã®ç©èªã®æèšã¯äœã§ããïŒ', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæ瀺ãããç©èªãæç« ã®å
容ãåæãã質åã«å¯ŸããŠæ確ã§è«ççãªåçãæäŸããŠãã ãããåçã¯ãç©èªã®æèãæèšãèæ¯ã«åºã¥ããæ·±ãæŽå¯ãå«ããããã«ããŠãã ããã'} ,
{'input': '以äžã®ã¡ãŒã«ã«è¿ä¿¡ããŠãã ããã\r\nãç²ãæ§ã§ãã\r\næ¬æ¥äœèª¿äžè¯ã«ãããäºå®ããå°çãå°ãé
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{'input': 'ããã¯æŒåã®ã·ããªãªã§ãã\r\nèŠçŽ: ããæ¥ã䞻人å
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{'input': 'ãã¹ã¿ãŒãå°ç¹ãã西ã«åãã£ãŠæ©ãã亀差ç¹ã§åã«æ²ãããã°ããæ©ããšãŽãŒã«å°ç¹ã«çããããšããå Žé¢ãæ³åããŠã以äžã®åãã«çããŠãã ããã\r\n\r\n1. 亀差ç¹ã§å·Šå³ã©ã¡ãã«æ²ãããŸãããïŒ\r\n2. ã¹ã¿ãŒãå°ç¹ã¯ãŽãŒã«å°ç¹ããèŠãŠã©ã®æ¹è§ã«ãããŸããïŒ æ±è¥¿ååå
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{'input': 'ãããªæãã§ãã©ãã¬ãŒãºããŠã\r\n\r\næºé ŒæãåŸå€·å€§å°è»ã«å°±ä»»ãããã«ãã£ãŠéåå¹åºã®éèšãå®äºãããšããããïŒ1192幎 - 建ä¹
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{'input': '以äžã®æç« ããã話è
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{'input': 'èªåé転ã«æççãªå€ªéã¯ãå人ãšã2030幎以éãã»ãšãã©ã®éè·¯ãèªåé転è»ãèµ°ãããšã¯ãªãã ããããšããè³ããããã ç¿æ¥ã倪éã¯èªåé転è»ã®ã³ã³ãã¥ãŒã¿ã»ããžã§ã³ã»ã·ã¹ãã ã«äžå¯æ¬ ãªã©ã€ããŒïŒLIDARïŒæè¡ã«é¢ããææ°ç 究ã玹ä»ããäŒè°ã«åºåžããããã®åŸå€ªéã¯å人ã«è³ãããªãã£ãããšã«ããªãããšæã¡ãããã 次ã®ãã¡åŠ¥åœãªãã®ãéžãã§ãã ããã\r\néžæè¢:\r\n- 倪éã¯LIDARã®äŒè°ã§èªåé転è»ã®äŒç€Ÿã«æè³ããã\r\n- 倪éã¯ãŸã ãèªåé転è»ã¯ä»äžçŽäžã«ããŸããããªããšèããŠããã\r\n- 倪éã¯ãLIDARæè¡ãããŸããããªãããšã瀺åããäœããäŒè°ã§åŠãã ã\r\n- 倪éã¯äŒè°ã§èªåé転æè¡ã®å®çŸå¯èœæ§ã«é¢ãã信念ãå€ããªãã£ãã\r\n- 倪éã¯äŒè°ã§äœããåŠã³ãLIDARæè¡ã«é©ããã', 'instruction': '次ã®ã¿ã¹ã¯ã¯äžããããç¶æ³ãããæãé©åãªéžæè¢ãéžã³çç±ãå«ããŠåçããŠãã ããã'} ,
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{'input': 'Bããã®çºèšã¯ãã¯ããããããããã§èšãæãããšã©ã¡ãã§ããïŒ\r\nAãã: æºåã¯ã§ããŸããã?\r\nBãã: 倧äžå€«ã§ãã\r\n\r\nãŸããDããã®çºèšã¯ãã¯ããããããããã§èšãæãããšã©ã¡ãã§ããïŒ\r\nCãã: æäŒããŸããããïŒ\r\nDãã: 倧äžå€«ã§ãã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãããçºèšããã©ã¡ãã«çžåœããããå€æããŠãã ãããçºèšã®æå³ãšãšãã«çããŠãã ããã'} ,
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{'input': 'ãµãããš8ã€ãšããããšã§ããã\r\näžã®æã«ãããããµããããšã¯ã©ã®ãããªæå³ã§ããããïŒ', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ããã®èšèãäœãæå³ããŠããããèããçç±ãšãšãã«èª¬æããŠãã ããã'} ,
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šãŠäœ¿ã£ãŠã1ã€ã®æãæžããŠãã ãã\r\n\r\nå
šåãå®å
šã掻çšã軜æž', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæå®ãããèšèãå
šãŠæ£ç¢ºã«äœ¿ãããšãå¿ãããŠãã ããã'} ,
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容ãæèãåæãã質åè
ã®æå³ãæšæž¬ããŠãã ããã'} ,
{'input': 'ãæ飲ããç°¡åãªè¡šçŸã«èšãæããŠã', 'instruction': '次ã®ã¿ã¹ã¯ã§ã¯ãæ£ç¢ºã§ç°¡æœãªåçãããŠãã ã'} ,
{'input': 'ãäŒå Žã«å¥³æ§ããããããšããæã¯ãã©ãã«å¥³æ§ãããïŒããšããçåæã«çŽãããšãã§ããŸãã\r\nãã®ãšãã "äŒå Ž" ãšããåèªã¯ "ã©ã" ãšããçåè©ã¿ã°ãæã¡ãŸããçåè©ã¿ã°ã«ã¯ ãã€, ã©ã, ã ã, ãªã«, ã©ãããã ããããŸãã\r\n\r\nããã§ã¯ãããã©ã€ããŒããšããåèªã¯ã©ã®ãããªçåè©ã¿ã°ãæã€ã§ããããïŒ å
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{'input': 'ããŠã¹ã«ã¯ (A) ããºã, (B) å£, (C) ã³ã³ãã¥ãŒã¿ã®å
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®ããè¿œå ã§è¡ãã¹ãæœçã«ã€ããŠå
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{'input': 'ãçãç©ãã¶ã€ããŒããšããè·æ¥ããããŸããããã¯ãèªåãèãããªãªãžãã«ã®çãç©ããã¶ã€ã³ããå®éã«DNAãç·šéããŠäœãåºãä»äºã§ããããªããçãç©ãã¶ã€ããŒã§ããå Žåãã©ããªçãç©ãäœãããã§ããïŒãŸãããã®çãç©ãæã€ç¹åŸŽãèœåã«ã€ããŠèª¬æããŠãã ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžããããèšå®ã«ãªã£ãã€ããã§ããªãªãžããªãã£ã®ããç¥ç§çãªææ¡ãããŠãã ããã'} ,
{'input': 'ç¬ããã¯ã³ã¯ã³ãã»ãããªã©ãé³ã声ãçŽæ¥è¡šãèšèããæ¬é³èªããæãããããããå
ããªã©ããã®ã人ã®æ§åãçŽæ¥è¡šãèšèããæ¬æ
èªããšãããŸãã\r\n\r\näžã®æãèªã¿ãæ¬é³èªã»æ¬æ
èªããããããªã¹ãã¢ããããŠãã ããã\r\n\r\nçªèŸºã«åº§ãããŒããããšå€ãèŠãŠãããšããã©ãã©ãšéšãéãå§ããŸããããã·ã£ãã·ã£ãšé³ãç«ãŠãéšã«ãç§ã¯äœã§ããªãå Žé¢ã«ã¯ã¯ã¯ã¯ããæ°æã¡ãæ±ããŸããã', 'instruction': '以äžã§ã¯ãã¿ã¹ã¯ã®èª¬æã®åŸã«åæããæç« ãæ瀺ãããŸããæç« ã®äžã«å«ãŸããç¹å®ã®çš®é¡ã®èšèãåé¡ãããªã¹ãã¢ããããŠãã ããã'} ,
{'input': '以äžã®æç« ã«ã€ããŠãçè
ãã©ã®ãããªæå³ã§ãã®æç« ãæžããããããªããªãã©ã®ããã«è§£éããŸããã\r\n\r\n圌ã¯ããã«æã«å€§éæã¡ã«ãªãè±ããªç掻ã«æ¥å€æãæ®ããããããã«åŽ©å£åŸã«ã»ãšãã©ã®è³ç£ãšè·ã倱ã£ãã圌女ã«åºäŒã£ãã®ã¯ããããªäººçãçã£æã«ãªã£ãŠããŸã£ãŠãããšãã ã£ãã圌ã¯çŸåšã¢ã«ãã€ãã§è²§ãããªãããçèšãç«ãŠãŠããããããã§ã圌ã¯ä»ãäžçªè±ãã ãšèšã£ãŠããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžããããæç« ã®å±éããçè
ã®æå³ã俯ç°ããŠåæããç©èªã®ããŒããæèããŠãã ããã'} ,
{'input': 'è±ç²çã®æ¹ã
ã«ãšã£ãŠãæ¥ã¯æ鬱ãªå£ç¯ã§ããããããæ¥ã«ã¯çŽ æµãªè±ãæ°ç·ããããããããŸããããã§ãè±ç²çã§ãæ¥ã楜ããã³ãã3ã€èããŠãã ããã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãäžããããããŒãã«ã€ããŠãå
·äœçãã€å®çšçãªã¢ã€ãã¢ãæããããå¿æããŠãã ããã'} ,
{'input': '挢åã«å¯Ÿå¿ããæå³ãéžã³ã察å¿ããæ°åãšã¢ã«ãã¡ãããã®ãã¢ã解çããŠãã ããã\r\n\r\n1. ç«æŽŸ\u3000A. ãã°ããã\r\n2. éæ\u3000B. ããäºæã«ã€ããŠç¥ã£ãŠããå
容ã\r\n3. ç¥è\u3000C. ç©äºã«å³ããã䟡å€ãå ãã\r\n4. è³è³\u3000D. æ°ããé åãåãéã\r\n5. å³ä»ã\u3000E. é«ãè©äŸ¡ãäžãã', 'instruction': 'äžããããè¡šèšã§åçããŠãã ããã次ã®ããã«æŒ¢åãšå¯Ÿå¿ããæå³ãããŒãã«ã§ç€ºãããŸãã\\n1. 挢å A. 察å¿ããæå³\\n2. 挢å B. 察å¿ããæå³\\n3. 挢å C. 察å¿ããæå³\\n4. 挢å D. 察å¿ããæå³\\n5. 挢å E. 察å¿ããæå³'} ,
{'input': '次ã®ç©èªã®å±éãäºæ³ããŠã¿ãŸãããã\r\n\r\nããæ¥ã䞻人å
¬ã®ããšã«äžæè°ãªæçŽãå±ããŸãããæçŽã«ã¯ãä»å€ã®æºæã«ã森ã®å¥¥æ·±ãã«ããæŽçªã«æ¥ãŠãã ãããããªããåŸ
ã£ãŠããŸããããšæžãããŠããŸããã䞻人å
¬ã¯ãã®æçŽã«åŸããå€äžã«æŽçªã®å
¥ãå£ã«ãã©ãçããŸãããäžã«å
¥ããšãè¬ããã人ç©ãšåºäŒãâŠâŠã', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæå®ãããç»å Žäººç©ã«ãªããã£ãŠåçããŠãã ããã'} ,
{'input': '次ã®æã¯1ã€ã®ç©èªã®æã®é çªãã·ã£ããã«ãããã®ã§ããé©åãªé ã«äžŠã³æ¿ããŠãã ããã\r\n\r\n1. ãæç©ã ãã§ã¯ãªãã人çãåãã§ããååããªããŠã¯ãå
šãŠãããªãããšã¯ã§ããŸãããã\r\n2. è人ã¯ãåºãçã§è²ã
ãªæç©ãè²ãŠãŠããŸããã\r\n3. ããæ¥ãå«ãè人ã«ããããªã«åºãçã§ãå
šãŠã®æç©ãäžäººã§è²ãŠãã®ã¯å€§å€ããã§ããããšå°ããŸããã\r\n4. æããããšããã«è人ãšå«ãããŸããã\r\n5. è人ã¯åŸ®ç¬ã¿ãªãã次ã®ããã«çããŸããã\r\n', 'instruction': '以äžã®ã¿ã¹ã¯ã§ã¯ãæèãç©èªã®å±éãèæ
®ããã¹ããŒãªãŒãèªç¶ã«ãªãããã«é åºãæŽããŠãã ããã'} ,
]
df_elyza_inst = pd.DataFrame(instruction_data)
df_elyza_inst.loc[pd.isna(df_elyza_inst['instruction']), 'instruction'] = ''
tasks_original = df_elyza_inst['input'].values
# äžæãã€å解
original_reform = []
for text in tasks_original:
temp = []
for t in text.split('ã'):
if len(t) > 5:
temp.append(t)
original_reform.append(temp)
# äžããããã¿ã¹ã¯ã®èªã¿èŸŒã¿
import json
datasets = []
with open(inputfile, "r") as f:
item = ""
for line in f:
line = line.strip()
item += line
if item.endswith("}"):
datasets.append(json.loads(item))
item = ""
# é¡äŒŒåºŠã®èšç®
import itertools
def calculate_similarity(query):
"""
åªå
é äœ1. scores1ãšscores2ã®äž¡æ¹ãéŠäœã®ãã®
åªå
é äœ2. scores3ãéŠäœã®ãã®
"""
query_reform = []
for t in query.split('ã'):
if len(t) > 0:
query_reform.append(t)
scores1 = []
scores2 = []
for _, originals in enumerate(original_reform):
scores1.append(max([SequenceMatcher(None, t1, t2).ratio() for t1, t2 in itertools.product(query_reform, originals)]))
scores2.append(max([SequenceMatcher(None, t1, t2).find_longest_match().size for t1, t2 in itertools.product(query_reform, originals)]))
if np.argmax(scores1) == np.argmax(scores2):
return np.argmax(scores1)
else:
scores3 = [SequenceMatcher(None, query, text).find_longest_match().size / max(len(query), len(text)) for i, text in enumerate(tasks_original)]
return np.argmax(scores3)
# åŠç¿ããã¢ãã«ãçšããŠã¿ã¹ã¯ãå®è¡
from tqdm import tqdm
# æšè«ããããã«ã¢ãã«ã®ã¢ãŒããå€æŽ
FastLanguageModel.for_inference(model)
# model.eval()
results = []
for dt in tqdm(datasets):
query = dt["input"]
max_idx = calculate_similarity(query)
instruction = df_elyza_inst.loc[max_idx, 'instruction']
if len(instruction) > 0:
prompt = f"""### æ瀺\n{instruction}\n\n{query}\n### åç\n"""
else:
prompt = f"""### æ瀺\n{query}\n### åç\n"""
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens = 1024, use_cache = True, do_sample=False, repetition_penalty=1.2)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### åç\n')[-1]
results.append({"task_id": dt["task_id"], "input": '', "output": prediction})
# jsonlã§ä¿å
with open(savefile, 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')