Datasets:
File size: 1,966 Bytes
4cf1966 1d936ff 4cf1966 1d936ff 4cf1966 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | {
"task": "expense line -> strict JSON record",
"seed": 0,
"counts": {
"train": 4000,
"val": 500,
"test": 500,
"pref": 600
},
"categories": [
"food",
"transport",
"groceries",
"utilities",
"entertainment",
"health",
"shopping",
"travel",
"other"
],
"train_merchants": [
"Swiggy",
"Zomato",
"Dominos",
"Starbucks",
"Cafe Coffee Day",
"KFC",
"Subway",
"Chai Point",
"Uber",
"Ola",
"Rapido",
"Metro Card",
"BluSmart",
"BigBasket",
"Blinkit",
"Zepto",
"DMart",
"Reliance Fresh",
"Airtel",
"Jio",
"Tata Power",
"BSNL",
"Act Fibernet",
"Netflix",
"Spotify",
"PVR Cinemas",
"BookMyShow",
"Apollo Pharmacy",
"Practo",
"Cult Fit",
"1mg",
"Amazon",
"Flipkart",
"Myntra",
"Croma",
"Decathlon",
"IndiGo",
"MakeMyTrip",
"IRCTC",
"Airbnb",
"Oyo Rooms",
"Razorpay",
"Paytm",
"PhonePe"
],
"held_out_merchants": [
"Faasos",
"Third Wave Coffee",
"Namma Yatri",
"Licious",
"Vodafone Idea",
"JioHotstar",
"MedPlus",
"Nykaa",
"Vistara",
"Cleartrip",
"Zerodha",
"Groww"
],
"pref_merchants": [
"Bikanervala",
"Barbeque Nation",
"Uber Auto",
"Country Delight",
"Tata Play",
"Dr Lal PathLabs",
"Ajio",
"Agoda"
],
"held_out_axes": [
"merchants",
"date phrasings",
"purpose phrasings",
"currency formats",
"split phrasings",
"sentence templates"
],
"system_prompt": "Convert the expense line into a JSON object with exactly these keys: amount (number), currency (one of INR, USD, EUR, GBP, JPY), merchant (string), category (one of food, transport, groceries, utilities, entertainment, health, shopping, travel, other), date (YYYY-MM-DD), split (integer, 1 if not stated), note (string or null). Reply with the JSON object only."
} |