frenchtext/bank-en-2401

Dataset Card for "bank en websites - 2401"

Dataset extracted from public websites by wordslab-webscraper in 2401:

• domain: bank
• language: en
• license: Apache 2.0

Dataset Sources

wordslab-webscraper follows the industry best practices for polite web scraping:

• clearly identifies itself as a known text indexing bot: "bingbot"
• doesn't try to hide the user IP address behind proxies
• doesn't try to circumvent bots protection solutions
• waits for a minimum delay between two pages to avoid generating too much load
• respects the website "robots.txt" indexing directives
• respects the web page Meta Robots HTML tag
• respects the web page X-Robots-Tag HTTP header
• respects the web page links rel=nofollow HTML attributes

The text data was extracted from the following websites:

Website	HTML pages	PDF docs	Words
bank.marksandspencer.com	464	152	795115
bankunderground.co.uk	754	6	1032786
country.db.com	591	206	2200174
home.barclays	0	572	11138629
monzo.com	490	37	696247
oaknorth.co.uk	328	27	392580
secure.cbonline.co.uk	95	0	41475
secure.ybonline.co.uk	56	0	28347
uk.virginmoney.com	248	12	277413
www.atombank.co.uk	301	20	603022
www.bankofchina.com	128	0	45205
www.bankofengland.co.uk	2426	407	10425072
www.bankofirelanduk.com	146	21	167325
www.bankofscotland.co.uk	278	1	15612
www.british-business-bank.co.uk	818	160	3267673
www.citizensadvice.org.uk	498	0	493400
www.co-operativebank.co.uk	837	490	3167940
www.ebrd.com	856	296	3114587
www.fbnbank.co.uk	29	26	85006
www.firstdirect.com	295	69	414668
www.handelsbanken.co.uk	274	0	160679
www.hsbc.co.uk	692	329	1210411
www.icicibank.co.uk	57	5	26792
www.independent.co.uk	1805	0	1382990
www.ing.com	734	0	4258827
www.metrobankonline.co.uk	375	171	1170969
www.nationwide.co.uk	1005	0	1359231
www.natwest.com	821	0	504462
www.natwestgroup.com	786	0	360711
www.postoffice.co.uk	443	0	501681
www.rbs.co.uk	365	0	229303
www.sainsburysbank.co.uk	262	23	320889
www.santander.co.uk	374	535	2591121
www.sc.com	668	1	514744
www.shawbrook.co.uk	727	105	1794105
www.smile.co.uk	196	38	233060
www.starlingbank.com	516	60	780370
www.tandem.co.uk	90	0	57046
www.tescobank.com	411	0	345395
www.ukib.org.uk	76	25	166834
www.ulsterbank.co.uk	125	0	74097
www.which.co.uk	1109	0	1049233
www.worldbank.org	242	0	110006

Uses

WARNING

• the text included in this dataset belongs to its original authors and is protected by copyright laws
• you are not allowed to use this dataset for anything else than training a large language model
• when using a large language model trained on this dataset, you will need to ensure that you comply with the law
• if you benefit from this large language model, you should try to share the value with the original text authors

wordslab-webscraper uses an advanced Html to text conversion algorithm optimized for long context language modeling:

• tries to recover the logical structure of the document from the Html or PDF layout
• preserves document / section / list / table grouping and nesting information
• deduplicates text at the website level while preserving the document structure

Each example in this dataset is a markdown text conversion of a full HTML page or PDF document:

• the document structure is preserved by markdown syntax: headers, lists, tables, paragraphs
• all duplicate paragraphs are removed

Dataset Structure

The dataset is divided in 3 splits:

• train: 80% of the data
• valid: 10% of the data
• test: 10% of the data

wordslab-webscraper generates one parquet file per website and per split.

The parquet files are named with the following pattern:

• bank_en_2401_[split]_[website].parquet

Note than you can load individual splits or websites with HuggingFace datasets using the following commands:

from datasets import load_dataset

# Load a single plit
dataset = load_dataset("namespace/bank-en-2401", split="train")

# Load a single website
data_files = { "train": "bank_en_2401_train_[website].parquet", "valid": "bank_en_2401_valid_[website].parquet", "test": "bank_en_2401_test_[website].parquet" }
dataset = load_dataset("namespace/bank-en-2401", data_files=data_files)

Each example in the dataset contains the text of a full web page or PDF document, with the following features:

• Uri: string
• ExtractedFromPDF: bool
• Timestamp: string
• Lang: string
• Title: string
• Text: string
• Words: int32
• AvgWordsLength: int32
• Chars: int32
• LetterChars: int32
• NumberChars: int32
• OtherChars: int32

Note that beause each example is a full page or document, the "Text" feature can be a pretty long string containing thousands of words (as measured by the "Words" feature): you will typically need to chunk it down to the context size of your large language model before using it.

Bias, Risks, and Limitations

This dataset is a direct extraction from the source websites.

It was not manually curated to remove misleading, offensive, or harmful content.

Please add a filtering step before using it to train a large language model if the source websites can't be trusted.

Dataset Card Contact

Please add a comment in the community section of this repository if you want the maintainer to add or remove websites from this dataset.