AutoTrain Dataset for project: full-dfsep23-xlmrobbase
Dataset Description
This dataset has been automatically processed by AutoTrain for project full-dfsep23-xlmrobbase.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"feat_Unnamed: 0.1": 0,
"feat_Unnamed: 0": 0,
"tokens": [
"terms",
"fm",
"door",
"Quinto",
"Di",
"Treviso",
"to",
"HKG",
"/",
"Tablo/",
"2",
"Plts",
"/",
"348",
"Kgs/",
"3.84",
"Cbm",
"/",
"Cargo",
"ready:",
"6",
"Jun",
"Ciao",
"Ale",
";",
"120*80*200",
"-",
"348",
"kgs.",
"Totali",
";",
"pick",
"up",
"address:",
";",
"Viale",
"dell'Industria,",
"26",
";",
"310",
"55",
"QUINTO",
"DI",
"TREVISO",
";",
"And",
"kindly",
"quote",
"upto",
"HKG",
"under",
"CPT",
"terms",
";",
"Grazie",
";",
"alessio",
";",
"Alessio",
"Rovetta",
";",
"Italy",
"Seafreight",
"Product",
"Manager",
";",
"[New",
"Logo",
"Mail]",
";",
"S.P.",
"14",
"Rivoltana",
"Km",
"9,500",
";",
"20060",
"-",
"Vignate",
"(MI)",
";",
"*si",
"accede",
"al",
"sito",
"da",
"via",
"Bruno",
"Buozzi",
"snc,",
"Liscate",
"(MI)",
";",
"Telefono:",
"+39",
"236766530",
";",
"Cellulare:",
"+39",
"3427670429",
";",
"E-mail:",
"a.rovetta@erixmar.com<mailto:a.rovetta@erixmar.com>",
";",
"In",
"relazione",
"all'entrata",
"in",
"vigore",
"del",
"cos\u00ec",
"detto",
"GDPR,",
"General",
"Data",
"Protection",
"Regulation,",
"anche",
"noi",
"in",
"ERIXMAR",
"SRL"
],
"tags": [
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0,
0,
12,
12,
12,
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5,
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]
},
{
"feat_Unnamed: 0.1": 412,
"feat_Unnamed: 0": 417,
"tokens": [
"Buongiorno",
";",
"Prego",
"quotare",
";",
"n.",
"1",
"CASSA",
"160",
"X",
"210",
"X",
"150",
"KG",
"1.50",
";",
";",
";",
"da",
"10127",
"Torino",
";",
"CIF",
"DAMMAM",
"PORT",
"-",
"SAUDI",
"ARABIA"
],
"tags": [
0,
0,
0,
0,
0,
0,
15,
10,
8,
8,
8,
8,
8,
21,
21,
0,
0,
0,
0,
11,
12,
0,
7,
5,
5,
5,
6,
6
]
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"feat_Unnamed: 0.1": "Value(dtype='int64', id=None)",
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(names=['O', 'commodity', 'company', 'delivery_cap', 'delivery_location', 'delivery_port', 'delivery_state', 'incoterms', 'measures', 'nan', 'package_type', 'pickup_cap', 'pickup_location', 'pickup_port', 'pickup_state', 'quantity', 'stackable', 'total_quantity', 'total_volume', 'total_weight', 'volume', 'weight'], id=None), length=-1, id=None)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
|---|---|
| train | 613 |
| valid | 269 |
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