Upload DistilBertForSequenceClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "unknown",
+    "1": "2theloo",
+    "2": "365 Retail Markets",
+    "3": "A4 Brescia Padova",
+    "4": "AMZS",
+    "5": "ASFINAG",
+    "6": "ASICS",
+    "7": "ATAC",
+    "8": "AWS",
+    "9": "Abacus Cooperativa",
+    "10": "Adyen",
+    "11": "Aelia Duty Free",
+    "12": "Aida",
+    "13": "Alcott",
+    "14": "Alipay",
+    "15": "American Eagle Outfitter",
+    "16": "American Express",
+    "17": "Amorino",
+    "18": "Appart'City",
+    "19": "Arabica Coffee",
+    "20": "Areas",
+    "21": "Arenal",
+    "22": "Atlantsol\u00eda",
+    "23": "Atm.it",
+    "24": "AutoZone",
+    "25": "Avianca Airlines",
+    "26": "Avoca",
+    "27": "B+B Parkhaus",
+    "28": "BCC Roma",
+    "29": "BNP Paribas",
+    "30": "BVG",
+    "31": "Basic-Fit",
+    "32": "BayWa",
+    "33": "Bellaflora",
+    "34": "Best-One",
+    "35": "Bi1",
+    "36": "Bico de Xeado",
+    "37": "BigMat",
+    "38": "Bingo City Center",
+    "39": "Bird",
+    "40": "Block House",
+    "41": "Blokker",
+    "42": "Boursorama",
+    "43": "Brandy Melville",
+    "44": "Bricorama",
+    "45": "Buc-ee's",
+    "46": "BudgetAir",
+    "47": "Bund.de",
+    "48": "Bureau Vall\u00e9e",
+    "49": "Butlers",
+    "50": "Bwin",
+    "51": "B\u00e4ckerei Hoefer",
+    "52": "B\u00e4ckerei Terbuyken",
+    "53": "B\u00e4ckerei Werning",
+    "54": "B\u00e4ckermeister Haferkamp",
+    "55": "CAP-Markt",
+    "56": "CBA",
+    "57": "CVMaker.uk",
+    "58": "Cafe & Bar Celona",
+    "59": "Cafe del Sol",
+    "60": "Caja Rural",
+    "61": "Cake Box",
+    "62": "Calpam",
+    "63": "Calvin Klein",
+    "64": "Canteen",
+    "65": "Careem",
+    "66": "Caribou Coffee",
+    "67": "Carter's",
+    "68": "Casa del Libro",
+    "69": "Cats Protection",
+    "70": "Centauro Rent a Car",
+    "71": "Centrakor",
+    "72": "Checkers",
+    "73": "Cinemark",
+    "74": "Cineplex",
+    "75": "Cinesa",
+    "76": "Cineworld",
+    "77": "CitizenM",
+    "78": "City Gross",
+    "79": "City Market",
+    "80": "City of Quebec",
+    "81": "Coin.it",
+    "82": "Columbus Cafe",
+    "83": "Copa Airlines",
+    "84": "Credit Engine",
+    "85": "Crunchyroll",
+    "86": "Curzon",
+    "87": "DGFIP",
+    "88": "DPD",
+    "89": "DPMCB",
+    "90": "Daiso",
+    "91": "Day Today",
+    "92": "Dec\u00f2",
+    "93": "DeepL",
+    "94": "Deiters",
+    "95": "Der B\u00e4cker Ruetz",
+    "96": "Der Rundfunkbeitrag",
+    "97": "Deutsche Bank",
+    "98": "Deutsche Post",
+    "99": "Deutsche Rentenversicherung",
+    "100": "Digital River",
+    "101": "Disney+",
+    "102": "Dnata",
+    "103": "Dom Lek\u00f3w",
+    "104": "Dr Martens",
+    "105": "Dussmann",
+    "106": "Dyson",
+    "107": "E.ON",
+    "108": "EE",
+    "109": "EasyPark",
+    "110": "Ebl-Naturkost",
+    "111": "Eharmony",
+    "112": "Elior",
+    "113": "Emirates Leisure Retail",
+    "114": "Emmerys",
+    "115": "Enchilada",
+    "116": "Engie",
+    "117": "Enrique Tomas",
+    "118": "Ergo",
+    "119": "EsclatOil",
+    "120": "EuroPark",
+    "121": "Everest",
+    "122": "FEBO",
+    "123": "FairPrice",
+    "124": "Fina",
+    "125": "Fitinn",
+    "126": "FlixBus",
+    "127": "Footasylum",
+    "128": "Four Seasons",
+    "129": "Free People",
+    "130": "Freie Tankstelle",
+    "131": "G La Dalle",
+    "132": "GNC",
+    "133": "Galaxias",
+    "134": "Galaxus",
+    "135": "Gall & Gall",
+    "136": "Gedimat",
+    "137": "Geldmaat",
+    "138": "Generali",
+    "139": "Getgo",
+    "140": "Getr\u00e4nke Hoffmann",
+    "141": "GoDaddy",
+    "142": "Goldcar",
+    "143": "Google Fi",
+    "144": "Greffe du Tribunal",
+    "145": "Gucci",
+    "146": "Guess",
+    "147": "HD Hotels",
+    "148": "HSBC",
+    "149": "Hamburg Airport",
+    "150": "Hannaford",
+    "151": "Hans im Gl\u00fcck",
+    "152": "Harald Nyborg",
+    "153": "Hebe",
+    "154": "Heinemann",
+    "155": "HelloFresh",
+    "156": "Hermes",
+    "157": "Hilton Garden Inn",
+    "158": "Hilton Garden Inn Hotel",
+    "159": "Hiper Centro",
+    "160": "Holland & Barrett",
+    "161": "Hollister Co.",
+    "162": "Hollywood Bowl",
+    "163": "Hotel Barcel\u00f3",
+    "164": "Hotel Mama Shelter",
+    "165": "Hotel Silken",
+    "166": "Hudson",
+    "167": "Hugo Boss",
+    "168": "HungryPanda",
+    "169": "IC Cash Services",
+    "170": "IHK",
+    "171": "IQOS",
+    "172": "ISS World",
+    "173": "In-N-Out Burger",
+    "174": "Insomnia Coffee",
+    "175": "InterContinental",
+    "176": "Interparking",
+    "177": "Iperal",
+    "178": "Italmark",
+    "179": "Jack & Jones",
+    "180": "Jacques' Wein-Depot",
+    "181": "Juan Valdez",
+    "182": "Jump Juice Bar",
+    "183": "JustAnswer",
+    "184": "K-Rauta",
+    "185": "KRAJ",
+    "186": "KTC",
+    "187": "Kastner & \u00d6hler",
+    "188": "Kinepolis",
+    "189": "Klarna",
+    "190": "Kramb\u00fa\u00f0",
+    "191": "Kritikos",
+    "192": "LNER",
+    "193": "La Sirena",
+    "194": "La Vie Claire",
+    "195": "Lacoste",
+    "196": "Lariviere",
+    "197": "Lastminute.com",
+    "198": "Le Crobag",
+    "199": "Le Five",
+    "200": "Lebara",
+    "201": "Lefties",
+    "202": "Legoland",
+    "203": "Leon Restaurants",
+    "204": "Les D\u00e9lices",
+    "205": "Levaduramadre",
+    "206": "Lindt",
+    "207": "Lloyds Farmacia",
+    "208": "Loblaws",
+    "209": "Localiza",
+    "210": "Lojas Renner",
+    "211": "Lotto",
+    "212": "Lovisa",
+    "213": "Lush Cosmetics",
+    "214": "MVG",
+    "215": "Macy's",
+    "216": "Maiora",
+    "217": "Manufactum",
+    "218": "Marco's Pizza",
+    "219": "Markant",
+    "220": "Markant Supermarkt",
+    "221": "Market Basket",
+    "222": "Massimo Dutti",
+    "223": "Medi-Market",
+    "224": "Meijer",
+    "225": "Meininger Hotels",
+    "226": "Mercado Extra",
+    "227": "Mercedes-Benz",
+    "228": "Merkur",
+    "229": "Meu Super",
+    "230": "Micromania",
+    "231": "Mladinska",
+    "232": "Mondadori Store",
+    "233": "Moto Motorway",
+    "234": "Mr. Bricolage",
+    "235": "M\u00f6belix",
+    "236": "NOZ",
+    "237": "Nah & Gut",
+    "238": "National Rail",
+    "239": "Nayax",
+    "240": "Netto Denmark",
+    "241": "Netto Marken-Discount",
+    "242": "Next",
+    "243": "Nordsee",
+    "244": "Norfa",
+    "245": "Notino",
+    "246": "O'Reilly Auto Parts",
+    "247": "O'Tacos",
+    "248": "OK Mobility",
+    "249": "OKay",
+    "250": "Ochsner Sport",
+    "251": "Old Wild West",
+    "252": "Ole & Steen",
+    "253": "Omio",
+    "254": "Omniva",
+    "255": "Oney",
+    "256": "OpenCor Vending",
+    "257": "P.F. Chang's",
+    "258": "POLOmarket",
+    "259": "PRIO",
+    "260": "Pad in Portugal",
+    "261": "Panet",
+    "262": "Panos",
+    "263": "Parken",
+    "264": "Patagonia",
+    "265": "Peter Pane",
+    "266": "Planet Fitness",
+    "267": "PlayStation",
+    "268": "Point Chaud",
+    "269": "Pokawa",
+    "270": "Poke House",
+    "271": "Polonez",
+    "272": "Post Luxembourg",
+    "273": "Potraviny",
+    "274": "Power.dk",
+    "275": "Proxim Supermercado",
+    "276": "Putka",
+    "277": "Qonto",
+    "278": "RTA",
+    "279": "Radatz",
+    "280": "Ralphs",
+    "281": "Real",
+    "282": "Riachuelo",
+    "283": "Ring",
+    "284": "Roblox",
+    "285": "Roku",
+    "286": "Rontec",
+    "287": "Rossopomodoro",
+    "288": "Ruch",
+    "289": "SFR",
+    "290": "Samsung",
+    "291": "SandwiChez",
+    "292": "Santagloria",
+    "293": "Saturn",
+    "294": "Second Cup",
+    "295": "Selfridges",
+    "296": "Servei Estaci\u00f3",
+    "297": "Sigma",
+    "298": "Silvan",
+    "299": "Six",
+    "300": "Slim Chickens",
+    "301": "Smart Parking",
+    "302": "Smarty Cashback",
+    "303": "Smullers",
+    "304": "Snappy Snaps",
+    "305": "Sokos Hotels",
+    "306": "Spearhead Taxis",
+    "307": "Spinneys",
+    "308": "Sp\u00e4tkauf",
+    "309": "Stadt Wien",
+    "310": "Stadt-Apotheke",
+    "311": "Star Tankstelle",
+    "312": "Steam",
+    "313": "Stroili Oro",
+    "314": "SumUp",
+    "315": "Suma Supermercados",
+    "316": "Super Muffato",
+    "317": "SuperFit",
+    "318": "Supermercado Bip Bip",
+    "319": "Swatch",
+    "320": "S\u00f8strene Grene",
+    "321": "TGI Friday's",
+    "322": "Tabac Presse",
+    "323": "Tallink",
+    "324": "Tam",
+    "325": "Tank & Rast",
+    "326": "Tank Ono",
+    "327": "Team Autohof",
+    "328": "TeeGschwendner",
+    "329": "Telef\u00f3nica",
+    "330": "Tenpin",
+    "331": "The ASH",
+    "332": "The Alchemist",
+    "333": "The Coffee Bean & Tea Leaf",
+    "334": "The Fresh Market",
+    "335": "The Good Burger",
+    "336": "The North Face",
+    "337": "The Range",
+    "338": "The niu",
+    "339": "Thomann",
+    "340": "Three",
+    "341": "Tibits",
+    "342": "TikTok",
+    "343": "Tom Tailor",
+    "344": "Toom",
+    "345": "Top Oil",
+    "346": "Totally Wicked",
+    "347": "Toyota",
+    "348": "Trader Joe's",
+    "349": "Transgourmet",
+    "350": "Transports Publics Genevois",
+    "351": "Transports Publics Lausannois",
+    "352": "Trenitalia",
+    "353": "Trgovina Krk",
+    "354": "Truffaut",
+    "355": "Turris",
+    "356": "Tu\u0161",
+    "357": "U Express",
+    "358": "U-Bahn",
+    "359": "UNICEF",
+    "360": "Unifree",
+    "361": "United Colors of Benetton",
+    "362": "Valvi",
+    "363": "Venchi",
+    "364": "VeniceBeach Fitness",
+    "365": "Vio.com",
+    "366": "Vival",
+    "367": "Vivari Coffee & Bakery",
+    "368": "Vivid Seats",
+    "369": "Voi Scooters",
+    "370": "V\u00ednb\u00fa\u00f0in",
+    "371": "Wallapop",
+    "372": "Walther Tankstelle",
+    "373": "Wasabi",
+    "374": "WeWork",
+    "375": "Weekday",
+    "376": "Wolt",
+    "377": "Women'secret",
+    "378": "Woolworth",
+    "379": "XXL Sports & Outdoor",
+    "380": "Yves Rocher",
+    "381": "Zaxby's",
+    "382": "Zoho",
+    "383": "Zolpan",
+    "384": "Zorbas Bakery",
+    "385": "Zurich Insurance",
+    "386": "beObank",
+    "387": "eSmoking World",
+    "388": "\u00d6oB",
+    "389": "\u0395\u03bb\u03af\u03bd"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "2theloo": 1,
+    "365 Retail Markets": 2,
+    "A4 Brescia Padova": 3,
+    "AMZS": 4,
+    "ASFINAG": 5,
+    "ASICS": 6,
+    "ATAC": 7,
+    "AWS": 8,
+    "Abacus Cooperativa": 9,
+    "Adyen": 10,
+    "Aelia Duty Free": 11,
+    "Aida": 12,
+    "Alcott": 13,
+    "Alipay": 14,
+    "American Eagle Outfitter": 15,
+    "American Express": 16,
+    "Amorino": 17,
+    "Appart'City": 18,
+    "Arabica Coffee": 19,
+    "Areas": 20,
+    "Arenal": 21,
+    "Atlantsol\u00eda": 22,
+    "Atm.it": 23,
+    "AutoZone": 24,
+    "Avianca Airlines": 25,
+    "Avoca": 26,
+    "B+B Parkhaus": 27,
+    "BCC Roma": 28,
+    "BNP Paribas": 29,
+    "BVG": 30,
+    "Basic-Fit": 31,
+    "BayWa": 32,
+    "Bellaflora": 33,
+    "Best-One": 34,
+    "Bi1": 35,
+    "Bico de Xeado": 36,
+    "BigMat": 37,
+    "Bingo City Center": 38,
+    "Bird": 39,
+    "Block House": 40,
+    "Blokker": 41,
+    "Boursorama": 42,
+    "Brandy Melville": 43,
+    "Bricorama": 44,
+    "Buc-ee's": 45,
+    "BudgetAir": 46,
+    "Bund.de": 47,
+    "Bureau Vall\u00e9e": 48,
+    "Butlers": 49,
+    "Bwin": 50,
+    "B\u00e4ckerei Hoefer": 51,
+    "B\u00e4ckerei Terbuyken": 52,
+    "B\u00e4ckerei Werning": 53,
+    "B\u00e4ckermeister Haferkamp": 54,
+    "CAP-Markt": 55,
+    "CBA": 56,
+    "CVMaker.uk": 57,
+    "Cafe & Bar Celona": 58,
+    "Cafe del Sol": 59,
+    "Caja Rural": 60,
+    "Cake Box": 61,
+    "Calpam": 62,
+    "Calvin Klein": 63,
+    "Canteen": 64,
+    "Careem": 65,
+    "Caribou Coffee": 66,
+    "Carter's": 67,
+    "Casa del Libro": 68,
+    "Cats Protection": 69,
+    "Centauro Rent a Car": 70,
+    "Centrakor": 71,
+    "Checkers": 72,
+    "Cinemark": 73,
+    "Cineplex": 74,
+    "Cinesa": 75,
+    "Cineworld": 76,
+    "CitizenM": 77,
+    "City Gross": 78,
+    "City Market": 79,
+    "City of Quebec": 80,
+    "Coin.it": 81,
+    "Columbus Cafe": 82,
+    "Copa Airlines": 83,
+    "Credit Engine": 84,
+    "Crunchyroll": 85,
+    "Curzon": 86,
+    "DGFIP": 87,
+    "DPD": 88,
+    "DPMCB": 89,
+    "Daiso": 90,
+    "Day Today": 91,
+    "Dec\u00f2": 92,
+    "DeepL": 93,
+    "Deiters": 94,
+    "Der B\u00e4cker Ruetz": 95,
+    "Der Rundfunkbeitrag": 96,
+    "Deutsche Bank": 97,
+    "Deutsche Post": 98,
+    "Deutsche Rentenversicherung": 99,
+    "Digital River": 100,
+    "Disney+": 101,
+    "Dnata": 102,
+    "Dom Lek\u00f3w": 103,
+    "Dr Martens": 104,
+    "Dussmann": 105,
+    "Dyson": 106,
+    "E.ON": 107,
+    "EE": 108,
+    "EasyPark": 109,
+    "Ebl-Naturkost": 110,
+    "Eharmony": 111,
+    "Elior": 112,
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