golden.json

{
 "model": "models/jet-v5",
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  "score": 1.2968,
  "noul": 1.4983
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   "question": {
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    "instructions": "What kind of entity is described?",
    "criteria": {
     "OfficeHolder": "a politician or office holder",
     "Plant": "a plant",
     "Company": "a company or business",
     "Film": "a film",
     "NaturalPlace": "a natural place such as a mountain, river or lake",
     "Artist": "an artist or musician",
     "MeanOfTransportation": "a vehicle, ship, aircraft or other means of transportation",
     "EducationalInstitution": "a school, college or university"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nDie Feststellung\nDie Feststellung is an East German film. It was released in 1958.\n</state>\n\nQuestion: What kind of entity is described?\nOptions:\nA: OfficeHolder: a politician or office holder\nB: Plant: a plant\nC: Company: a company or business\nD: Film: a film\nE: NaturalPlace: a natural place such as a mountain, river or lake\nF: Artist: an artist or musician\nG: MeanOfTransportation: a vehicle, ship, aircraft or other means of transportation\nH: EducationalInstitution: a school, college or university\nAnswer with the label of the best option (A-H).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "choice_emotion_1",
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    "instructions": "Which emotion does the writer express?",
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     "joy": "the writer feels happy, content or pleased",
     "fear": "the writer feels afraid, anxious or nervous",
     "love": "the writer feels love, affection or tenderness",
     "anger": "the writer feels angry, irritated or resentful",
     "surprise": "the writer feels surprised or amazed"
    }
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\ni walked into the dawn treader feeling fairly skeptical and walked out with three great books one was a hardcover book in japanese that i picked up for my mom for\n</state>\n\nQuestion: Which emotion does the writer express?\nOptions:\nA: sadness: the writer feels sad, down or hurt\nB: joy: the writer feels happy, content or pleased\nC: fear: the writer feels afraid, anxious or nervous\nD: love: the writer feels love, affection or tenderness\nE: anger: the writer feels angry, irritated or resentful\nF: surprise: the writer feels surprised or amazed\nAnswer with the label of the best option (A-F).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "confidence": 0.3101
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  {
   "name": "choice_ag_news_2",
   "state": "Soyuz crew docks craft manually with international space station A US-Russian crew had to turn off autopilot yesterday and manually moor its Soyuz spacecraft to the international space station after an unidentified glitch resulted ",
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    "type": "choice",
    "instructions": "Classify the topic of this text.",
    "criteria": {
     "business": "business, markets, companies and the economy",
     "sports": "sports, athletes, games and competitions",
     "scitech": "science and technology",
     "world": "world news, politics, international affairs"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nSoyuz crew docks craft manually with international space station A US-Russian crew had to turn off autopilot yesterday and manually moor its Soyuz spacecraft to the international space station after an unidentified glitch resulted \n</state>\n\nQuestion: Classify the topic of this text.\nOptions:\nA: business: business, markets, companies and the economy\nB: sports: sports, athletes, games and competitions\nC: scitech: science and technology\nD: world: world news, politics, international affairs\nAnswer with the label of the best option (A-D).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
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  },
  {
   "name": "choice_mnli_3",
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    "premise": "The thrones on view here date from a visit by George V and Queen Mary in 1927.",
    "hypothesis": "The thrones existed in 1927."
   },
   "question": {
    "type": "choice",
    "instructions": "How does the hypothesis relate to the premise?",
    "criteria": {
     "contradiction": "the hypothesis is definitely false given the premise",
     "neutral": "the hypothesis might be true or false; the premise does not settle it",
     "entailment": "the hypothesis is definitely true given the premise"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"premise\": \"The thrones on view here date from a visit by George V and Queen Mary in 1927.\",\n  \"hypothesis\": \"The thrones existed in 1927.\"\n}\n</state>\n\nQuestion: How does the hypothesis relate to the premise?\nOptions:\nA: contradiction: the hypothesis is definitely false given the premise\nB: neutral: the hypothesis might be true or false; the premise does not settle it\nC: entailment: the hypothesis is definitely true given the premise\nAnswer with the label of the best option (A-C).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
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    "confidence": 0.7852
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  },
  {
   "name": "choice_banking77_4",
   "state": "I've been waiting for 3 days, why is my withdrawal still pending?",
   "question": {
    "type": "choice",
    "instructions": "Which intent best matches this message?",
    "criteria": {
     "transfer_not_received_by_recipient": "transfer not received by recipient",
     "transfer_fee_charged": "transfer fee charged",
     "top_up_by_bank_transfer_charge": "top up by bank transfer charge",
     "supported_cards_and_currencies": "supported cards and currencies",
     "lost_or_stolen_phone": "lost or stolen phone",
     "topping_up_by_card": "topping up by card",
     "top_up_limits": "top up limits",
     "transaction_charged_twice": "transaction charged twice",
     "terminate_account": "terminate account",
     "card_arrival": "card arrival",
     "balance_not_updated_after_bank_transfer": "balance not updated after bank transfer",
     "pending_cash_withdrawal": "pending cash withdrawal",
     "wrong_amount_of_cash_received": "wrong amount of cash received",
     "change_pin": "change pin",
     "contactless_not_working": "contactless not working"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nI've been waiting for 3 days, why is my withdrawal still pending?\n</state>\n\nQuestion: Which intent best matches this message?\nOptions:\nA: transfer_not_received_by_recipient: transfer not received by recipient\nB: transfer_fee_charged: transfer fee charged\nC: top_up_by_bank_transfer_charge: top up by bank transfer charge\nD: supported_cards_and_currencies: supported cards and currencies\nE: lost_or_stolen_phone: lost or stolen phone\nF: topping_up_by_card: topping up by card\nG: top_up_limits: top up limits\nH: transaction_charged_twice: transaction charged twice\nI: terminate_account: terminate account\nJ: card_arrival: card arrival\nK: balance_not_updated_after_bank_transfer: balance not updated after bank transfer\nL: pending_cash_withdrawal: pending cash withdrawal\nM: wrong_amount_of_cash_received: wrong amount of cash received\nN: change_pin: change pin\nO: contactless_not_working: contactless not working\nAnswer with the label of the best option (A-O).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "choice": "pending_cash_withdrawal",
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  {
   "name": "choice_massive_5",
   "state": "start my play list",
   "question": {
    "type": "choice",
    "instructions": "What does the user want?",
    "criteria": {
     "recommendation_movies": "recommendation movies",
     "music_likeness": "music likeness",
     "general_joke": "general joke",
     "lists_query": "lists query",
     "calendar_query": "calendar query",
     "cooking_query": "cooking query",
     "datetime_convert": "datetime convert",
     "general_quirky": "general quirky",
     "email_sendemail": "email sendemail",
     "play_music": "play music",
     "alarm_set": "alarm set",
     "audio_volume_up": "audio volume up",
     "iot_hue_lightchange": "iot hue lightchange",
     "email_querycontact": "email querycontact",
     "qa_maths": "qa maths",
     "news_query": "news query",
     "iot_wemo_off": "iot wemo off",
     "play_game": "play game",
     "cooking_recipe": "cooking recipe",
     "audio_volume_other": "audio volume other",
     "audio_volume_down": "audio volume down",
     "social_query": "social query",
     "email_addcontact": "email addcontact",
     "play_radio": "play radio",
     "music_settings": "music settings",
     "recommendation_locations": "recommendation locations",
     "iot_coffee": "iot coffee",
     "calendar_remove": "calendar remove",
     "iot_hue_lightdim": "iot hue lightdim",
     "qa_currency": "qa currency",
     "transport_taxi": "transport taxi",
     "play_audiobook": "play audiobook",
     "iot_wemo_on": "iot wemo on",
     "audio_volume_mute": "audio volume mute",
     "transport_traffic": "transport traffic",
     "qa_factoid": "qa factoid",
     "datetime_query": "datetime query",
     "transport_query": "transport query",
     "qa_definition": "qa definition",
     "email_query": "email query",
     "recommendation_events": "recommendation events",
     "iot_hue_lighton": "iot hue lighton",
     "alarm_remove": "alarm remove",
     "music_dislikeness": "music dislikeness",
     "takeaway_query": "takeaway query",
     "lists_createoradd": "lists createoradd",
     "social_post": "social post",
     "calendar_set": "calendar set",
     "transport_ticket": "transport ticket",
     "play_podcasts": "play podcasts",
     "iot_hue_lightoff": "iot hue lightoff",
     "iot_hue_lightup": "iot hue lightup",
     "lists_remove": "lists remove",
     "general_greet": "general greet",
     "music_query": "music query",
     "iot_cleaning": "iot cleaning",
     "takeaway_order": "takeaway order",
     "qa_stock": "qa stock",
     "weather_query": "weather query",
     "alarm_query": "alarm query"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nstart my play list\n</state>\n\nQuestion: What does the user want?\nOptions:\nA: recommendation_movies: recommendation movies\nB: music_likeness: music likeness\nC: general_joke: general joke\nD: lists_query: lists query\nE: calendar_query: calendar query\nF: cooking_query: cooking query\nG: datetime_convert: datetime convert\nH: general_quirky: general quirky\nI: email_sendemail: email sendemail\nJ: play_music: play music\nK: alarm_set: alarm set\nL: audio_volume_up: audio volume up\nM: iot_hue_lightchange: iot hue lightchange\nN: email_querycontact: email querycontact\nO: qa_maths: qa maths\nP: news_query: news query\nQ: iot_wemo_off: iot wemo off\nR: play_game: play game\nS: cooking_recipe: cooking recipe\nT: audio_volume_other: audio volume other\nU: audio_volume_down: audio volume down\nV: social_query: social query\nW: email_addcontact: email addcontact\nX: play_radio: play radio\nY: music_settings: music settings\nZ: recommendation_locations: recommendation locations\nAA: iot_coffee: iot coffee\nAB: calendar_remove: calendar remove\nAC: iot_hue_lightdim: iot hue lightdim\nAD: qa_currency: qa currency\nAE: transport_taxi: transport taxi\nAF: play_audiobook: play audiobook\nAG: iot_wemo_on: iot wemo on\nAH: audio_volume_mute: audio volume mute\nAI: transport_traffic: transport traffic\nAJ: qa_factoid: qa factoid\nAK: datetime_query: datetime query\nAL: transport_query: transport query\nAM: qa_definition: qa definition\nAN: email_query: email query\nAO: recommendation_events: recommendation events\nAP: iot_hue_lighton: iot hue lighton\nAQ: alarm_remove: alarm remove\nAR: music_dislikeness: music dislikeness\nAS: takeaway_query: takeaway query\nAT: lists_createoradd: lists createoradd\nAU: social_post: social post\nAV: calendar_set: calendar set\nAW: transport_ticket: transport ticket\nAX: play_podcasts: play podcasts\nAY: iot_hue_lightoff: iot hue lightoff\nAZ: iot_hue_lightup: iot hue lightup\nBA: lists_remove: lists remove\nBB: general_greet: general greet\nBC: music_query: music query\nBD: iot_cleaning: iot cleaning\nBE: takeaway_order: takeaway order\nBF: qa_stock: qa stock\nBG: weather_query: weather query\nBH: alarm_query: alarm query\nAnswer with the label of the best option (A-BH).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
    "type": "choice",
    "choice": "play_music",
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  {
   "name": "choice_mnli_6",
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"premise\": \"Under DOD's revised policy, it is still difficult to determine if a product should enter product demonstration with a stable design.\",\n  \"hypothesis\": \"The revised policy has greatly changed many of the DOD's determinations.\"\n}\n</state>\n\nQuestion: How does the hypothesis relate to the premise?\nOptions:\nA: neutral: the hypothesis might be true or false; the premise does not settle it\nB: contradiction: the hypothesis is definitely false given the premise\nC: entailment: the hypothesis is definitely true given the premise\nAnswer with the label of the best option (A-C).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "question": {
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    "criteria": {
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     "business": "business, markets, companies and the economy",
     "scitech": "science and technology",
     "world": "world news, politics, international affairs"
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   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nRBI dy governor Mohan to be finance secy (Reuters) Reuters - The government named Reserve Bank of India deputy governor Rakesh Mohan as the country's new finance secretary, an official statement said on Tuesday.\n</state>\n\nQuestion: Which category best fits this text?\nOptions:\nA: sports: sports, athletes, games and competitions\nB: business: business, markets, companies and the economy\nC: scitech: science and technology\nD: world: world news, politics, international affairs\nAnswer with the label of the best option (A-D).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "label_token_ids": [
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   "temperature": 1.0,
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   "answer": {
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  },
  {
   "name": "choice_mnli_8",
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    "hypothesis": "Fiscal year 1998 was a successful year for the Postal Service, in terms of finances."
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   "question": {
    "type": "choice",
    "instructions": "How does the hypothesis relate to the premise?",
    "criteria": {
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     "neutral": "the hypothesis might be true or false; the premise does not settle it"
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"premise\": \"And, as you probably know, in presenting estimates of its future needs, the Postal Service used financial and operational data from fiscal year 1998 as a benchmark.\",\n  \"hypothesis\": \"Fiscal year 1998 was a successful year for the Postal Service, in terms of finances.\"\n}\n</state>\n\nQuestion: How does the hypothesis relate to the premise?\nOptions:\nA: contradiction: the hypothesis is definitely false given the premise\nB: entailment: the hypothesis is definitely true given the premise\nC: neutral: the hypothesis might be true or false; the premise does not settle it\nAnswer with the label of the best option (A-C).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  },
  {
   "name": "choice_emotion_9",
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   "question": {
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    "criteria": {
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     "anger": "the writer feels angry, irritated or resentful",
     "fear": "the writer feels afraid, anxious or nervous",
     "joy": "the writer feels happy, content or pleased",
     "love": "the writer feels love, affection or tenderness",
     "surprise": "the writer feels surprised or amazed"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\ni feel angry or resentful all i need do is remind myself that each day sober has been made possible by a fellowship which supports me all the way\n</state>\n\nQuestion: Which emotion does the writer express?\nOptions:\nA: sadness: the writer feels sad, down or hurt\nB: anger: the writer feels angry, irritated or resentful\nC: fear: the writer feels afraid, anxious or nervous\nD: joy: the writer feels happy, content or pleased\nE: love: the writer feels love, affection or tenderness\nF: surprise: the writer feels surprised or amazed\nAnswer with the label of the best option (A-F).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "probabilities": {
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  {
   "name": "score_stsb_0",
   "state": {
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   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
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     "different topics but share a few details",
     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
    ]
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"Fifty-seven senators, including 24 Republicans, have signed the letter.\",\n  \"sentence_2\": \"Of those who signed the letter, 57 are senators, including 24 Republicans.\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "score_yelp_1",
   "state": "Giant CVS, has all your basic necessities, open all the time.\\n\\nDefinitely go here over a casino sundries store. Cheaper and easier. You can stock the room with bottled water if you need too.\\n\\nBonus points from me because they have a Chase ATM which I saved me some ATM fees since I might have forgotten to hit the bank before my latest weekend extravaganza...oops.\\n\\nThe fact that I can buy vodka at CVS in Vegas still amazes me. Much better deal then you'll find at the hotels.",
   "question": {
    "type": "score",
    "instructions": "How positive is this review?",
    "criteria": [
     "very negative",
     "negative",
     "mixed or neutral",
     "positive",
     "very positive"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nGiant CVS, has all your basic necessities, open all the time.\\n\\nDefinitely go here over a casino sundries store. Cheaper and easier. You can stock the room with bottled water if you need too.\\n\\nBonus points from me because they have a Chase ATM which I saved me some ATM fees since I might have forgotten to hit the bank before my latest weekend extravaganza...oops.\\n\\nThe fact that I can buy vodka at CVS in Vegas still amazes me. Much better deal then you'll find at the hotels.\n</state>\n\nQuestion: How positive is this review?\nScale (lowest to highest):\n0: very negative\n1: negative\n2: mixed or neutral\n3: positive\n4: very positive\nAnswer with the level number (0-4).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "score_stsb_2",
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   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
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     "different topics but share a few details",
     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"A woman is cooking something.\",\n  \"sentence_2\": \"A woman is pouring ingredients into a frying pan.\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  },
  {
   "name": "score_yelp_3",
   "state": "This place is not a bad glorified fast food style eatery. There is a fair variety of items to choose from, however, the food can be hit or miss. Most recently I grabbed a burger and fries with a fountain drink. Fries were fresh and hot. Burger wasn't terrible but had too much of a char taste. It is a bit pricey for what is being offered as well. Burgers, fries and a drink hits just under $18.",
   "question": {
    "type": "score",
    "instructions": "How positive is this review?",
    "criteria": [
     "very negative",
     "negative",
     "mixed or neutral",
     "positive",
     "very positive"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nThis place is not a bad glorified fast food style eatery. There is a fair variety of items to choose from, however, the food can be hit or miss. Most recently I grabbed a burger and fries with a fountain drink. Fries were fresh and hot. Burger wasn't terrible but had too much of a char taste. It is a bit pricey for what is being offered as well. Burgers, fries and a drink hits just under $18.\n</state>\n\nQuestion: How positive is this review?\nScale (lowest to highest):\n0: very negative\n1: negative\n2: mixed or neutral\n3: positive\n4: very positive\nAnswer with the level number (0-4).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  },
  {
   "name": "score_stsb_4",
   "state": {
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    "sentence_2": "White House backs gay marriage"
   },
   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
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     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"Yvette Cooper backs gay marriage bill\",\n  \"sentence_2\": \"White House backs gay marriage\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "labels": [
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    "2",
    "3",
    "4",
    "5"
   ],
   "label_token_ids": [
    15,
    16,
    17,
    18,
    19,
    20
   ],
   "logits": [
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    6.4688,
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   "temperature": 1.2968,
   "probabilities": [
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   "answer": {
    "type": "score",
    "score": 3.0947,
    "level": "roughly equivalent, some important details differ",
    "probabilities": [
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    "confidence": 0.3404
   }
  },
  {
   "name": "score_stsb_5",
   "state": {
    "sentence_1": "in september 2007 jordan signed an accord with the united states aimed at supporting the peaceful development of the jordan's nascent nuclear program.",
    "sentence_2": "in september 2007 the united states signed an accord with jordan aimed at supporting the peaceful development of jodran's new nuclear program."
   },
   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
     "completely unrelated",
     "different topics but share a few details",
     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"in september 2007 jordan signed an accord with the united states aimed at supporting the peaceful development of the jordan's nascent nuclear program.\",\n  \"sentence_2\": \"in september 2007 the united states signed an accord with jordan aimed at supporting the peaceful development of jodran's new nuclear program.\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
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    "score": 4.2074,
    "level": "mostly equivalent, only minor details differ",
    "probabilities": [
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    "confidence": 0.425
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  },
  {
   "name": "score_stsb_6",
   "state": {
    "sentence_1": "A horse standing near a gate with another horse in the background.",
    "sentence_2": "A group of river barges with trees in the background."
   },
   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
     "completely unrelated",
     "different topics but share a few details",
     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"A horse standing near a gate with another horse in the background.\",\n  \"sentence_2\": \"A group of river barges with trees in the background.\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "labels": [
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   "label_token_ids": [
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   "logits": [
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  },
  {
   "name": "score_yelp_7",
   "state": "The Duce is probably the coolest bar/restaurant/smoothie shop/boxing ring/clothing store/trailer you'll ever go to!\\n\\nIn the mood for mind-bowing short rib sliders? Check.\\n\\nNeed a new pair of Chuck's? Check.\\n\\nThirsty for an ice-cold PBR? Check.\\n\\nFeel like punching someone in the face? Check-ish...\\n\\nMy only gripe would be drink prices. If you're down to slam some PBR or old school Budweiser, the you're drinking for $3 a pop. Fancy a Mojito - that'll run you about $12 for a 6oz-er. Focus on the cheap drinks and the amazing food, and you will love this place forever!",
   "question": {
    "type": "score",
    "instructions": "How positive is this review?",
    "criteria": [
     "very negative",
     "negative",
     "mixed or neutral",
     "positive",
     "very positive"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nThe Duce is probably the coolest bar/restaurant/smoothie shop/boxing ring/clothing store/trailer you'll ever go to!\\n\\nIn the mood for mind-bowing short rib sliders? Check.\\n\\nNeed a new pair of Chuck's? Check.\\n\\nThirsty for an ice-cold PBR? Check.\\n\\nFeel like punching someone in the face? Check-ish...\\n\\nMy only gripe would be drink prices. If you're down to slam some PBR or old school Budweiser, the you're drinking for $3 a pop. Fancy a Mojito - that'll run you about $12 for a 6oz-er. Focus on the cheap drinks and the amazing food, and you will love this place forever!\n</state>\n\nQuestion: How positive is this review?\nScale (lowest to highest):\n0: very negative\n1: negative\n2: mixed or neutral\n3: positive\n4: very positive\nAnswer with the level number (0-4).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "type": "score",
    "score": 3.5959,
    "level": "very positive",
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  },
  {
   "name": "score_yelp_8",
   "state": "A straight man...in a gay bar. My friends ponder this question every time I go to a place like this. They think that just by walking through the door you become a card carrying lesbian, and you know what, I'm glad.\\n\\nThe general narrow mindedness of people keeps the \\\"lads\\\" out of this place, leaving nothing but the friendliest people in the world. I think alot of girls come here to just so they don't get hit on by guy's and can dance in peace.\\n\\nThis place is just chilled out, i've never seen a fight or anyone even raising there voice. The drinks are expensive, but you're paying extra for a hassle free night.",
   "question": {
    "type": "score",
    "instructions": "How positive is this review?",
    "criteria": [
     "very negative",
     "negative",
     "mixed or neutral",
     "positive",
     "very positive"
    ]
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nA straight man...in a gay bar. My friends ponder this question every time I go to a place like this. They think that just by walking through the door you become a card carrying lesbian, and you know what, I'm glad.\\n\\nThe general narrow mindedness of people keeps the \\\"lads\\\" out of this place, leaving nothing but the friendliest people in the world. I think alot of girls come here to just so they don't get hit on by guy's and can dance in peace.\\n\\nThis place is just chilled out, i've never seen a fight or anyone even raising there voice. The drinks are expensive, but you're paying extra for a hassle free night.\n</state>\n\nQuestion: How positive is this review?\nScale (lowest to highest):\n0: very negative\n1: negative\n2: mixed or neutral\n3: positive\n4: very positive\nAnswer with the level number (0-4).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "score_stsb_9",
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   },
   "question": {
    "type": "score",
    "instructions": "How similar in meaning are the two sentences?",
    "criteria": [
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     "not equivalent but on the same topic",
     "roughly equivalent, some important details differ",
     "mostly equivalent, only minor details differ",
     "completely equivalent in meaning"
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"sentence_1\": \"Two cartoon rabbits are kissing.\",\n  \"sentence_2\": \"A rabbit is kissing another rabbit.\"\n}\n</state>\n\nQuestion: How similar in meaning are the two sentences?\nScale (lowest to highest):\n0: completely unrelated\n1: different topics but share a few details\n2: not equivalent but on the same topic\n3: roughly equivalent, some important details differ\n4: mostly equivalent, only minor details differ\n5: completely equivalent in meaning\nAnswer with the level number (0-5).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "type": "score",
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  {
   "name": "noul_boolq_0",
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   "question": {
    "type": "noul",
    "instructions": "Is the holly and the ivy a hymn?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n``The Holly and the Ivy'' is a traditional British folk Christmas carol. The song is catalogued as Roud Folk Song Index No. 514.\n</state>\n\nQuestion: Is the holly and the ivy a hymn?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "type": "noul",
    "probability": 0.2812,
    "confidence": 0.1429
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  {
   "name": "noul_emotion_1",
   "state": "i might be needing quite sometimes to let this feelings fade away but i wont make you feel insecure or disturb or uncomfortable",
   "question": {
    "type": "noul",
    "instructions": "Does the writer express this: the writer feels happy, content or pleased?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\ni might be needing quite sometimes to let this feelings fade away but i wont make you feel insecure or disturb or uncomfortable\n</state>\n\nQuestion: Does the writer express this: the writer feels happy, content or pleased?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  },
  {
   "name": "noul_ag_news_2",
   "state": "Agassi Wins in Straight Sets in Madrid (AP) AP - Andre Agassi breezed past fellow American Vince Spadea 6-1, 6-3 Thursday night to reach the quarterfinals in the Madrid Masters.",
   "question": {
    "type": "noul",
    "instructions": "Is this article about sports, athletes, games and competitions?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nAgassi Wins in Straight Sets in Madrid (AP) AP - Andre Agassi breezed past fellow American Vince Spadea 6-1, 6-3 Thursday night to reach the quarterfinals in the Madrid Masters.\n</state>\n\nQuestion: Is this article about sports, athletes, games and competitions?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
    "type": "noul",
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  },
  {
   "name": "noul_mnli_3",
   "state": {
    "premise": "The Association of Certified Fraud Examiners consists of approximately 25,000 certified fraud examiners and associated members in 70 different countries.",
    "hypothesis": "The Association of Certified Fraud Examiners was set up with a broad set of goals."
   },
   "question": {
    "type": "noul",
    "instructions": "Does the premise imply the hypothesis?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"premise\": \"The Association of Certified Fraud Examiners consists of approximately 25,000 certified fraud examiners and associated members in 70 different countries.\",\n  \"hypothesis\": \"The Association of Certified Fraud Examiners was set up with a broad set of goals.\"\n}\n</state>\n\nQuestion: Does the premise imply the hypothesis?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "answer": {
    "type": "noul",
    "probability": 0.0942,
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  },
  {
   "name": "noul_banking77_4",
   "state": "Can I choose what card I can use",
   "question": {
    "type": "noul",
    "instructions": "Is the customer asking about: visa or mastercard?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nCan I choose what card I can use\n</state>\n\nQuestion: Is the customer asking about: visa or mastercard?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "type": "noul",
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  },
  {
   "name": "noul_yelp_5",
   "state": "With only a 30 min lunch window, I have to make use of the time I have.  Great Wall is fast and efficient.  \\n\\nFirst time, I had to come in.  I didn't have a menu or phone number.  I made a quick lunch order.  Great pricing and a big selection.  There really wasn't a wait.  Took me 10 minutes.  Service is quick and while waiting, I noticed other folks calling in orders.  Which I took to grab a menu to stash at work for next time.  Place was packed while I was there.  \\n\\nI ordered a sweet and sour pork, and chow mein lunch special.  My chow mein was huge, way too much for lunch.  Came with an eggroll and some fried rice.  My coworker ate his whole box up.  I asked him how was it, and he goes \\\"that hit the spot.\\\"  He's down to grab another box again next time.  The chow mein was decently made.  Not too heavy on the flavor which is great.  I don't like how some places make it like Panda Express with tons of oil/grease.  \\n\\nThey knew how to package the togo boxes.  Added utensils and sauces/spices.  Parking may be a pain during rush hour.  I browsed their menu and say mushu pork..  Not too many places make this item, so I'll def have to head back to grab me one.",
   "question": {
    "type": "noul",
    "instructions": "Is the customer satisfied?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nWith only a 30 min lunch window, I have to make use of the time I have.  Great Wall is fast and efficient.  \\n\\nFirst time, I had to come in.  I didn't have a menu or phone number.  I made a quick lunch order.  Great pricing and a big selection.  There really wasn't a wait.  Took me 10 minutes.  Service is quick and while waiting, I noticed other folks calling in orders.  Which I took to grab a menu to stash at work for next time.  Place was packed while I was there.  \\n\\nI ordered a sweet and sour pork, and chow mein lunch special.  My chow mein was huge, way too much for lunch.  Came with an eggroll and some fried rice.  My coworker ate his whole box up.  I asked him how was it, and he goes \\\"that hit the spot.\\\"  He's down to grab another box again next time.  The chow mein was decently made.  Not too heavy on the flavor which is great.  I don't like how some places make it like Panda Express with tons of oil/grease.  \\n\\nThey knew how to package the togo boxes.  Added utensils and sauces/spices.  Parking may be a pain during rush hour.  I browsed their menu and say mushu pork..  Not too many places make this item, so I'll def have to head back to grab me one.\n</state>\n\nQuestion: Is the customer satisfied?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "state": "Dear Sally Ancheta: I hope you and your kids are not lard  a$$es and out of shape, otherwise I might need the almighty city council and part-time useless mayor to legislate a new law banning Dunkin Donuts, Applebees, McDs, Wendys, etc etc etc.... until the government deems you're FIT to live.",
   "question": {
    "type": "noul",
    "instructions": "Does this comment contain a threat?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nDear Sally Ancheta: I hope you and your kids are not lard  a$$es and out of shape, otherwise I might need the almighty city council and part-time useless mayor to legislate a new law banning Dunkin Donuts, Applebees, McDs, Wendys, etc etc etc.... until the government deems you're FIT to live.\n</state>\n\nQuestion: Does this comment contain a threat?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "question": {
    "type": "noul",
    "instructions": "Does the premise imply the hypothesis?"
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"premise\": \"The fluid in it splashed into Mrs. Vandemeyer's face, and during her momentary gasp, Tuppence's right hand shot out and grasped the revolver where it lay on the edge of the washstand.\",\n  \"hypothesis\": \"Mrs. Vandemeyer was holding the gun when fluid was splashed on her face. \"\n}\n</state>\n\nQuestion: Does the premise imply the hypothesis?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "noul_emotion_8",
   "state": "i feel sorry seeing my parents",
   "question": {
    "type": "noul",
    "instructions": "Does the writer express this: the writer feels happy, content or pleased?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\ni feel sorry seeing my parents\n</state>\n\nQuestion: Does the writer express this: the writer feels happy, content or pleased?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "name": "noul_ag_news_9",
   "state": "Madonna Makes Israeli Midnight Pilgrimage JERUSALEM - Pop diva Madonna made a midnight pilgrimage to a Jerusalem cemetery early Sunday, holding a mystical candlelit ceremony at the grave of a Jewish sage.    The singer is in Israel on a five-day spiritual quest along with 2,000 other students of Kabbalah, or Jewish mysticism...",
   "question": {
    "type": "noul",
    "instructions": "Is this article about world news, politics, international affairs?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nMadonna Makes Israeli Midnight Pilgrimage JERUSALEM - Pop diva Madonna made a midnight pilgrimage to a Jerusalem cemetery early Sunday, holding a mystical candlelit ceremony at the grave of a Jewish sage.    The singer is in Israel on a five-day spiritual quest along with 2,000 other students of Kabbalah, or Jewish mysticism...\n</state>\n\nQuestion: Is this article about world news, politics, international affairs?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "json_state",
   "state": {
    "user": "Zoë",
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    "tickets": [
     {
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      "text": "Refund? Charged twice 💳"
     }
    ],
    "active": true
   },
   "question": {
    "type": "noul",
    "instructions": "Is the customer asking for money back?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n{\n  \"user\": \"Zoë\",\n  \"plan\": \"pro\",\n  \"tickets\": [\n    {\n      \"id\": 17,\n      \"text\": \"Refund? Charged twice 💳\"\n    }\n  ],\n  \"active\": true\n}\n</state>\n\nQuestion: Is the customer asking for money back?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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    "type": "noul",
    "probability": 0.2448,
    "confidence": 0.197
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  },
  {
   "name": "list_state",
   "state": [
    "User: my build fails on ARM",
    "Agent: which compiler?",
    "User: clang 19, see log above"
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   "question": {
    "type": "choice",
    "instructions": "What should the agent do next?",
    "criteria": {
     "ask_log": "ask for the full build log",
     "close": "close the ticket",
     "escalate": "hand to a human"
    }
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nUser: my build fails on ARM\n\nAgent: which compiler?\n\nUser: clang 19, see log above\n</state>\n\nQuestion: What should the agent do next?\nOptions:\nA: ask_log: ask for the full build log\nB: close: close the ticket\nC: escalate: hand to a human\nAnswer with the label of the best option (A-C).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "non_ascii_state",
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    "criteria": [
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   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\n東京の配達が三日遅れています。至急対応してください！ Merci d'avance — très urgent.\n</state>\n\nQuestion: How urgent is this message?\nScale (lowest to highest):\n0: low\n1: medium\n2: high\nAnswer with the level number (0-2).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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  {
   "name": "two_letter_labels",
   "state": "The invoice lists a 30-day payment term but the contract says 45 days.",
   "question": {
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    "instructions": "Which category fits best?",
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     "cat_09": "category number 9",
     "cat_10": "category number 10",
     "cat_11": "category number 11",
     "cat_12": "category number 12",
     "cat_13": "category number 13",
     "cat_14": "category number 14",
     "cat_15": "category number 15",
     "cat_16": "category number 16",
     "cat_17": "category number 17",
     "cat_18": "category number 18",
     "cat_19": "category number 19",
     "cat_20": "category number 20",
     "cat_21": "category number 21",
     "cat_22": "category number 22",
     "cat_23": "category number 23",
     "cat_24": "category number 24",
     "cat_25": "category number 25",
     "cat_26": "category number 26",
     "cat_27": "category number 27",
     "cat_28": "category number 28",
     "cat_29": "category number 29",
     "cat_30": "category number 30",
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     "cat_38": "category number 38",
     "cat_39": "category number 39",
     "contract_mismatch": "terms differ between documents"
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   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nThe invoice lists a 30-day payment term but the contract says 45 days.\n</state>\n\nQuestion: Which category fits best?\nOptions:\nA: cat_00: category number 0\nB: cat_01: category number 1\nC: cat_02: category number 2\nD: cat_03: category number 3\nE: cat_04: category number 4\nF: cat_05: category number 5\nG: cat_06: category number 6\nH: cat_07: category number 7\nI: cat_08: category number 8\nJ: cat_09: category number 9\nK: cat_10: category number 10\nL: cat_11: category number 11\nM: cat_12: category number 12\nN: cat_13: category number 13\nO: cat_14: category number 14\nP: cat_15: category number 15\nQ: cat_16: category number 16\nR: cat_17: category number 17\nS: cat_18: category number 18\nT: cat_19: category number 19\nU: cat_20: category number 20\nV: cat_21: category number 21\nW: cat_22: category number 22\nX: cat_23: category number 23\nY: cat_24: category number 24\nZ: cat_25: category number 25\nAA: cat_26: category number 26\nAB: cat_27: category number 27\nAC: cat_28: category number 28\nAD: cat_29: category number 29\nAE: cat_30: category number 30\nAF: cat_31: category number 31\nAG: cat_32: category number 32\nAH: cat_33: category number 33\nAI: cat_34: category number 34\nAJ: cat_35: category number 35\nAK: cat_36: category number 36\nAL: cat_37: category number 37\nAM: cat_38: category number 38\nAN: cat_39: category number 39\nAO: contract_mismatch: terms differ between documents\nAnswer with the label of the best option (A-AO).<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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handled in 0 ms, status 200\nlog line 4851: request handled in 1 ms, status 200\nlog line 4852: request handled in 2 ms, status 200\nlog line 4853: request handled in 3 ms, status 200\nlog line 4854: request handled in 4 ms, status 200\nlog line 4855: request handled in 5 ms, status 200\nlog line 4856: request handled in 6 ms, status 200\nlog line 4857: request handled in 7 ms, status 200\nlog line 4858: request handled in 8 ms, status 200\nlog line 4859: request handled in 9 ms, status 200\nlog line 4860: request handled in 10 ms, status 200\nlog line 4861: request handled in 11 ms, status 200\nlog line 4862: request handled in 12 ms, status 200\nlog line 4863: request handled in 13 ms, status 200\nlog line 4864: request handled in 14 ms, status 200\nlog line 4865: request handled in 15 ms, status 200\nlog line 4866: request handled in 16 ms, status 200\nlog line 4867: request handled in 17 ms, status 200\nlog line 4868: request handled in 18 ms, status 200\nlog line 4869: request handled in 19 ms, status 200\nlog line 4870: request handled in 20 ms, status 200\nlog line 4871: request handled in 21 ms, status 200\nlog line 4872: request handled in 22 ms, status 200\nlog line 4873: request handled in 23 ms, status 200\nlog line 4874: request handled in 24 ms, status 200\nlog line 4875: request handled in 25 ms, status 200\nlog line 4876: request handled in 26 ms, status 200\nlog line 4877: request handled in 27 ms, status 200\nlog line 4878: request handled in 28 ms, status 200\nlog line 4879: request handled in 29 ms, status 200\nlog line 4880: request handled in 30 ms, status 200\nlog line 4881: request handled in 31 ms, status 200\nlog line 4882: request handled in 32 ms, status 200\nlog line 4883: request handled in 33 ms, status 200\nlog line 4884: request handled in 34 ms, status 200\nlog line 4885: request handled in 35 ms, status 200\nlog line 4886: request handled in 36 ms, status 200\nlog line 4887: request handled in 37 ms, status 200\nlog line 4888: request handled in 38 ms, status 200\nlog line 4889: request handled in 39 ms, status 200\nlog line 4890: request handled in 40 ms, status 200\nlog line 4891: request handled in 41 ms, status 200\nlog line 4892: request handled in 42 ms, status 200\nlog line 4893: request handled in 43 ms, status 200\nlog line 4894: request handled in 44 ms, status 200\nlog line 4895: request handled in 45 ms, status 200\nlog line 4896: request handled in 46 ms, status 200\nlog line 4897: request handled in 47 ms, status 200\nlog line 4898: request handled in 48 ms, status 200\nlog line 4899: request handled in 49 ms, status 200\nlog line 4900: request handled in 50 ms, status 200\nlog line 4901: request handled in 51 ms, status 200\nlog line 4902: request handled in 52 ms, status 200\nlog line 4903: request handled in 53 ms, status 200\nlog line 4904: request handled in 54 ms, status 200\nlog line 4905: request handled in 55 ms, status 200\nlog line 4906: request handled in 56 ms, status 200\nlog line 4907: request handled in 57 ms, status 200\nlog line 4908: request handled in 58 ms, status 200\nlog line 4909: request handled in 59 ms, status 200\nlog line 4910: request handled in 60 ms, status 200\nlog line 4911: request handled in 61 ms, status 200\nlog line 4912: request handled in 62 ms, status 200\nlog line 4913: request handled in 63 ms, status 200\nlog line 4914: request handled in 64 ms, status 200\nlog line 4915: request handled in 65 ms, status 200\nlog line 4916: request handled in 66 ms, status 200\nlog line 4917: request handled in 67 ms, status 200\nlog line 4918: request handled in 68 ms, status 200\nlog line 4919: request handled in 69 ms, status 200\nlog line 4920: request handled in 70 ms, status 200\nlog line 4921: request handled in 71 ms, status 200\nlog line 4922: request handled in 72 ms, status 200\nlog line 4923: request handled in 73 ms, status 200\nlog line 4924: request handled in 74 ms, status 200\nlog line 4925: request handled in 75 ms, status 200\nlog line 4926: request handled in 76 ms, status 200\nlog line 4927: request handled in 77 ms, status 200\nlog line 4928: request handled in 78 ms, status 200\nlog line 4929: request handled in 79 ms, status 200\nlog line 4930: request handled in 80 ms, status 200\nlog line 4931: request handled in 81 ms, status 200\nlog line 4932: request handled in 82 ms, status 200\nlog line 4933: request handled in 83 ms, status 200\nlog line 4934: request handled in 84 ms, status 200\nlog line 4935: request handled in 85 ms, status 200\nlog line 4936: request handled in 86 ms, status 200\nlog line 4937: request handled in 87 ms, status 200\nlog line 4938: request handled in 88 ms, status 200\nlog line 4939: request handled in 89 ms, status 200\nlog line 4940: request handled in 90 ms, status 200\nlog line 4941: request handled in 91 ms, status 200\nlog line 4942: request handled in 92 ms, status 200\nlog line 4943: request handled in 93 ms, status 200\nlog line 4944: request handled in 94 ms, status 200\nlog line 4945: request handled in 95 ms, status 200\nlog line 4946: request handled in 96 ms, status 200\nlog line 4947: request handled in 0 ms, status 200\nlog line 4948: request handled in 1 ms, status 200\nlog line 4949: request handled in 2 ms, status 200\nlog line 4950: request handled in 3 ms, status 200\nlog line 4951: request handled in 4 ms, status 200\nlog line 4952: request handled in 5 ms, status 200\nlog line 4953: request handled in 6 ms, status 200\nlog line 4954: request handled in 7 ms, status 200\nlog line 4955: request handled in 8 ms, status 200\nlog line 4956: request handled in 9 ms, status 200\nlog line 4957: request handled in 10 ms, status 200\nlog line 4958: request handled in 11 ms, status 200\nlog line 4959: request handled in 12 ms, status 200\nlog line 4960: request handled in 13 ms, status 200\nlog line 4961: request handled in 14 ms, status 200\nlog line 4962: request handled in 15 ms, status 200\nlog line 4963: request handled in 16 ms, status 200\nlog line 4964: request handled in 17 ms, status 200\nlog line 4965: request handled in 18 ms, status 200\nlog line 4966: request handled in 19 ms, status 200\nlog line 4967: request handled in 20 ms, status 200\nlog line 4968: request handled in 21 ms, status 200\nlog line 4969: request handled in 22 ms, status 200\nlog line 4970: request handled in 23 ms, status 200\nlog line 4971: request handled in 24 ms, status 200\nlog line 4972: request handled in 25 ms, status 200\nlog line 4973: request handled in 26 ms, status 200\nlog line 4974: request handled in 27 ms, status 200\nlog line 4975: request handled in 28 ms, status 200\nlog line 4976: request handled in 29 ms, status 200\nlog line 4977: request handled in 30 ms, status 200\nlog line 4978: request handled in 31 ms, status 200\nlog line 4979: request handled in 32 ms, status 200\nlog line 4980: request handled in 33 ms, status 200\nlog line 4981: request handled in 34 ms, status 200\nlog line 4982: request handled in 35 ms, status 200\nlog line 4983: request handled in 36 ms, status 200\nlog line 4984: request handled in 37 ms, status 200\nlog line 4985: request handled in 38 ms, status 200\nlog line 4986: request handled in 39 ms, status 200\nlog line 4987: request handled in 40 ms, status 200\nlog line 4988: request handled in 41 ms, status 200\nlog line 4989: request handled in 42 ms, status 200\nlog line 4990: request handled in 43 ms, status 200\nlog line 4991: request handled in 44 ms, status 200\nlog line 4992: request handled in 45 ms, status 200\nlog line 4993: request handled in 46 ms, status 200\nlog line 4994: request handled in 47 ms, status 200\nlog line 4995: request handled in 48 ms, status 200\nlog line 4996: request handled in 49 ms, status 200\nlog line 4997: request handled in 50 ms, status 200\nlog line 4998: request handled in 51 ms, status 200\nlog line 4999: request handled in 52 ms, status 200",
   "question": {
    "type": "noul",
    "instructions": "Does the log contain a server error?"
   },
   "prompt": "<|im_start|>system\nYou are Jet, a decision model. Read the state and the question, then answer with exactly one label from the allowed labels.<|im_end|>\n<|im_start|>user\n<state>\nlog line 0: request handled in 0 ms, status 200\nlog line 1: request handled in 1 ms, status 200\nlog line 2: request handled in 2 ms, status 200\nlog line 3: request handled in 3 ms, status 200\nlog line 4: request handled in 4 ms, status 200\nlog line 5: request handled in 5 ms, status 200\nlog line 6: request handled in 6 ms, status 200\nlog line 7: request handled in 7 ms, status 200\nlog line 8: request handled in 8 ms, status 200\nlog line 9: request handled in 9 ms, status 200\nlog line 10: request handled in 10 ms, status 200\nlog line 11: request handled in 11 ms, status 200\nlog line 12: request handled in 12 ms, status 200\nlog line 13: request handled in 13 ms, status 200\nlog line 14: request handled in 14 ms, status 200\nlog line 15: request handled in 15 ms, status 200\nlog line 16: request handled in 16 ms, status 200\nlog line 17: request handled in 17 ms, status 200\nlog line 18: request handled in 18 ms, status 200\nlog line 19: request handled in 19 ms, status 200\nlog line 20: request handled in 20 ms, status 200\nlog line 21: request handled in 21 ms, status 200\nlog line 22: request handled in 22 ms, status 200\nlog line 23: request handled in 23 ms, status 200\nlog line 24: request handled in 24 ms, status 200\nlog line 25: request handled in 25 ms, status 200\nlog line 26: request handled in 26 ms, status 200\nlog line 27: request handled in 27 ms, status 200\nlog line 28: request handled in 28 ms, status 200\nlog line 29: request handled in 29 ms, status 200\nlog line 30: request handled in 30 ms, status 200\nlog line 31: request handled in 31 ms, status 200\nlog line 32: request handled in 32 ms, status 200\nlog line 33: request handled in 33 ms, status 200\nlog line 34: request handled in 34 ms, status 200\nlog line 35: request handled in 35 ms, status 200\nlog line 36: request handled in 36 ms, status 200\nlog line 37: request handled in 37 ms, status 200\nlog line 38: request handled in 38 ms, status 200\nlog line 39: request handled in 39 ms, status 200\nlog line 40: request handled in 40 ms, status 200\nlog line 41: request handled in 41 ms, status 200\nlog line 42: request handled in 42 ms, status 200\nlog line 43: request handled in 43 ms, status 200\nlog line 44: request handled in 44 ms, status 200\nlog line 45: request handled in 45 ms, status 200\nlog line 46: request handled in 46 ms, status 200\nlog line 47: request handled in 47 ms, status 200\nlog line 48: request handled in 48 ms, status 200\nlog line 49: request handled in 49 ms, status 200\nlog line 50: request handled in 50 ms, status 200\nlog line 51: request handled in 51 ms, status 200\nlog line 52: request handled in 52 ms, status 200\nlog line 53: request handled in 53 ms, status 200\nlog line 54: request handled in 54 ms, status 200\nlog line 55: request handled in 55 ms, status 200\nlog line 56: request handled in 56 ms, status 200\nlog line 57: request handled in 57 ms, status 200\nlog line 58: request handled in 58 ms, status 200\nlog line 59: request handled in 59 ms, status 200\nlog line 60: request handled in 60 ms, status 200\nlog line 61: request handled in 61 ms, status 200\nlog line 62: request handled in 62 ms, status 200\nlog line 63: request handled in 63 ms, status 200\nlog line 64: request handled in 64 ms, status 200\nlog line 65: request handled in 65 ms, status 200\nlog line 66: request handled in 66 ms, status 200\nlog line 67: request handled in 67 ms, status 200\nlog line 68: request handled in 68 ms, status 200\nlog line 69: request handled in 69 ms, status 200\nlog line 70: request handled in 70 ms, status 200\nlog line 71: request handled in 71 ms, status 200\nlog line 72: request handled in 72 ms, status 200\nlog line 73: request handled in 73 ms, status 200\nlog line 74: request handled in 74 ms, status 200\nlog line 75: request handled in 75 ms, status 200\nlog line 76: request handled in 76 ms, status 200\nlog line 77: request handled in 77 ms, status 200\nlog line 78: request handled in 78 ms, status 200\nlog line 79: request handled in 79 ms, status 200\nlog line 80: request handled in 80 ms, status 200\nlog line 81: request handled in 81 ms, status 200\nlog line 82: request handled in 82 ms, status 200\nlog line 83: request handled in 83 ms, status 200\nlog line 84: request handled in 84 ms, status 200\nlog line 85: request handled in 85 ms, status 200\nlog line 86: request handled in 86 ms, status 200\nlog line 87: request handled in 87 ms, status 200\nlog line 88: request handled in 88 ms, status 200\nlog line 89: request handled in 89 ms, status 200\nlog line 90: request handled in 90 ms, status 200\nlog line 91: request handled in 91 ms, status 200\nlog line 92: request handled in 92 ms, status 200\nlog line 93: request handled in 93 ms, status 200\nlog line 94: request handled in 94 ms, status 200\nlog line 95: request handled in 95 ms, status 200\nlog line 96: request handled in 96 ms, status 200\nlog line 97: request handled in 0 ms, status 200\nlog line 98: request handled in 1 ms, status 200\nlog line 99: request handled in 2 ms, status 200\nlog line 100: request handled in 3 ms, status 200\nlog line 101: request handled in 4 ms, status 200\nlog line 102: request handled in 5 ms, status 200\nlog line 103: request handled in \n[...]\n handled in 56 ms, status 200\nlog line 4907: request handled in 57 ms, status 200\nlog line 4908: request handled in 58 ms, status 200\nlog line 4909: request handled in 59 ms, status 200\nlog line 4910: request handled in 60 ms, status 200\nlog line 4911: request handled in 61 ms, status 200\nlog line 4912: request handled in 62 ms, status 200\nlog line 4913: request handled in 63 ms, status 200\nlog line 4914: request handled in 64 ms, status 200\nlog line 4915: request handled in 65 ms, status 200\nlog line 4916: request handled in 66 ms, status 200\nlog line 4917: request handled in 67 ms, status 200\nlog line 4918: request handled in 68 ms, status 200\nlog line 4919: request handled in 69 ms, status 200\nlog line 4920: request handled in 70 ms, status 200\nlog line 4921: request handled in 71 ms, status 200\nlog line 4922: request handled in 72 ms, status 200\nlog line 4923: request handled in 73 ms, status 200\nlog line 4924: request handled in 74 ms, status 200\nlog line 4925: request handled in 75 ms, status 200\nlog line 4926: request handled in 76 ms, status 200\nlog line 4927: request handled in 77 ms, status 200\nlog line 4928: request handled in 78 ms, status 200\nlog line 4929: request handled in 79 ms, status 200\nlog line 4930: request handled in 80 ms, status 200\nlog line 4931: request handled in 81 ms, status 200\nlog line 4932: request handled in 82 ms, status 200\nlog line 4933: request handled in 83 ms, status 200\nlog line 4934: request handled in 84 ms, status 200\nlog line 4935: request handled in 85 ms, status 200\nlog line 4936: request handled in 86 ms, status 200\nlog line 4937: request handled in 87 ms, status 200\nlog line 4938: request handled in 88 ms, status 200\nlog line 4939: request handled in 89 ms, status 200\nlog line 4940: request handled in 90 ms, status 200\nlog line 4941: request handled in 91 ms, status 200\nlog line 4942: request handled in 92 ms, status 200\nlog line 4943: request handled in 93 ms, status 200\nlog line 4944: request handled in 94 ms, status 200\nlog line 4945: request handled in 95 ms, status 200\nlog line 4946: request handled in 96 ms, status 200\nlog line 4947: request handled in 0 ms, status 200\nlog line 4948: request handled in 1 ms, status 200\nlog line 4949: request handled in 2 ms, status 200\nlog line 4950: request handled in 3 ms, status 200\nlog line 4951: request handled in 4 ms, status 200\nlog line 4952: request handled in 5 ms, status 200\nlog line 4953: request handled in 6 ms, status 200\nlog line 4954: request handled in 7 ms, status 200\nlog line 4955: request handled in 8 ms, status 200\nlog line 4956: request handled in 9 ms, status 200\nlog line 4957: request handled in 10 ms, status 200\nlog line 4958: request handled in 11 ms, status 200\nlog line 4959: request handled in 12 ms, status 200\nlog line 4960: request handled in 13 ms, status 200\nlog line 4961: request handled in 14 ms, status 200\nlog line 4962: request handled in 15 ms, status 200\nlog line 4963: request handled in 16 ms, status 200\nlog line 4964: request handled in 17 ms, status 200\nlog line 4965: request handled in 18 ms, status 200\nlog line 4966: request handled in 19 ms, status 200\nlog line 4967: request handled in 20 ms, status 200\nlog line 4968: request handled in 21 ms, status 200\nlog line 4969: request handled in 22 ms, status 200\nlog line 4970: request handled in 23 ms, status 200\nlog line 4971: request handled in 24 ms, status 200\nlog line 4972: request handled in 25 ms, status 200\nlog line 4973: request handled in 26 ms, status 200\nlog line 4974: request handled in 27 ms, status 200\nlog line 4975: request handled in 28 ms, status 200\nlog line 4976: request handled in 29 ms, status 200\nlog line 4977: request handled in 30 ms, status 200\nlog line 4978: request handled in 31 ms, status 200\nlog line 4979: request handled in 32 ms, status 200\nlog line 4980: request handled in 33 ms, status 200\nlog line 4981: request handled in 34 ms, status 200\nlog line 4982: request handled in 35 ms, status 200\nlog line 4983: request handled in 36 ms, status 200\nlog line 4984: request handled in 37 ms, status 200\nlog line 4985: request handled in 38 ms, status 200\nlog line 4986: request handled in 39 ms, status 200\nlog line 4987: request handled in 40 ms, status 200\nlog line 4988: request handled in 41 ms, status 200\nlog line 4989: request handled in 42 ms, status 200\nlog line 4990: request handled in 43 ms, status 200\nlog line 4991: request handled in 44 ms, status 200\nlog line 4992: request handled in 45 ms, status 200\nlog line 4993: request handled in 46 ms, status 200\nlog line 4994: request handled in 47 ms, status 200\nlog line 4995: request handled in 48 ms, status 200\nlog line 4996: request handled in 49 ms, status 200\nlog line 4997: request handled in 50 ms, status 200\nlog line 4998: request handled in 51 ms, status 200\nlog line 4999: request handled in 52 ms, status 200\n</state>\n\nQuestion: Does the log contain a server error?\nAnswer yes or no.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
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   "labels": [
    "no",
    "yes"
   ],
   "label_token_ids": [
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   "logits": [
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   "temperature": 1.4983,
   "probabilities": [
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   "answer": {
    "type": "noul",
    "probability": 0.0694,
    "confidence": 0.6365
   }
  }
 ],
 "decide": [
  {
   "state": "I was charged twice this month and nobody answers my emails.",
   "questions": {
    "topic": {
     "type": "choice",
     "instructions": "What is the primary issue?",
     "criteria": {
      "billing": "billing or payment problem",
      "bug": "the product is broken"
     }
    },
    "severity": {
     "type": "score",
     "instructions": "How urgent is this?",
     "criteria": [
      "routine",
      "handle today",
      "urgent",
      "critical, about to churn"
     ]
    },
    "escalate": {
     "type": "noul",
     "instructions": "Escalate to a human immediately?"
    }
   },
   "answers": {
    "topic": {
     "type": "choice",
     "choice": "bug",
     "probabilities": {
      "billing": 0.0619,
      "bug": 0.9381
     },
     "confidence": 0.6652
    },
    "severity": {
     "type": "score",
     "score": 0.6108,
     "level": "routine",
     "probabilities": [
      0.6375,
      0.1614,
      0.1538,
      0.0472
     ],
     "confidence": 0.2689
    },
    "escalate": {
     "type": "noul",
     "probability": 0.2207,
     "confidence": 0.2386
    }
   }
  },
  {
   "state": {
    "tool": "shell",
    "command": "rm -rf ./build && make",
    "cwd": "/home/dev/project"
   },
   "questions": {
    "safe": {
     "type": "noul",
     "instructions": "Is this tool call safe to run without asking?"
    },
    "risk": {
     "type": "score",
     "instructions": "How destructive could this be?",
     "criteria": [
      "harmless",
      "recoverable",
      "data loss",
      "system damage"
     ]
    }
   },
   "answers": {
    "safe": {
     "type": "noul",
     "probability": 0.4072,
     "confidence": 0.025
    },
    "risk": {
     "type": "score",
     "score": 1.8551,
     "level": "data loss",
     "probabilities": [
      0.0921,
      0.1853,
      0.4978,
      0.2247
     ],
     "confidence": 0.1237
    }
   }
  }
 ]
}