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675de29d-9ed1-480a-83c2-63bf95867472 | completed | ba989482-6521-407f-8b55-c31b656044dc | They cover all kind of instruments with Cryptocurrency just being the main focus of the company. Stocks: you get to enjoy access to trade on the potential movement of top stocks shares like US: BAC, MS, APPL; JP: Toyota, Sony, NTT; DE: BAY and ALD. | [
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05ac4baf-b238-44c3-8b8b-98f451f7a92b | completed | 6243d74a-a546-4386-b591-ca15b7e492cc | Cryptocurrency: cryptocurrencies instrument provided are up to 16 in numbers, which included all the major crosses like (BTC/ETH, ETH/USD, and BTC/USD). | [
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5e58a964-83cf-41fd-a3a7-b18c4be39874 | completed | f4e12552-859c-4078-9b77-d100683d22db | Forex – with over 60 Currencies pair being traded on the platform, you have access to all major crosses such GBP/USD, EUR/USD and minor Forex pairs like AUD/NZD. | [
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9cb9588a-6140-415d-884e-e9a05163bf2d | completed | 72eb1c90-3ccb-49f3-b7f6-bc5b409fe447 | 6. There are certain words that you should understand that are related to the business that you are in. These are known as incoterms. If you want to study up, visit the website for The International Chamber of Commerce. | [
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fa7e90e0-d3f1-41d2-a798-073e0c6f5f9b | completed | 5d54ab8d-0499-4d36-879b-07b416249af2 | Diversified primary fund investments with top-tier buyout, venture capital and special situations sponsors, as well as opportunistic co-investment and secondary investments with selected managers. | [
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9a7b1f0e-b1dc-4948-98b9-36f1e9207222 | completed | 1b37cd96-55df-4579-8fb0-bc1ef3e86355 | Typically, a balance sheet, income statement, cash flow statement, and partial budget or enterprise budgets are included in a business plan. | [
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cf07e97d-7c36-4af3-95bc-686c9909e09e | completed | 1f1e47a7-0c01-47a7-ace7-f8a74809f478 | completed items. sold items. deals & savings. . vintage fashion la gear gold tennis shoes 'dance /womens. | [
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9dd61357-f728-4420-be5a-852a84c1bc86 | completed | 175027c2-28f3-4f24-ac29-1a7f427d6c66 | Part 2 will cover Energy and Real Estate sector stocks that announced dividend increases last week, while Part 3 will cover dividend increases from the remaining sectors. | [
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5be9aca1-ea1e-4033-b07c-fedb01898129 | completed | d63daa67-c581-4059-9a35-bc91b4799b9a | Stocks rated 5 stars or better are worthy of further analysis. | [
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22d2c7da-373d-4546-8dbb-956ef5363098 | completed | ee11ea45-44d4-4bba-a7c8-0e90093a6d2d | But really, Bitcoin, Ethereum, and Ripple are the three that are far in the lead in terms of adoption. | [
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35227b7e-9dbf-48cd-a1a2-e15c2f1f13c7 | pending | 0c70548e-7eeb-496f-aa50-540c69218ab9 | 1. Page Title: Is your page title distinctive and descriptive? 2. Information Structure: Is your content broken into logical segments? 3. Headings: Are there descriptive headings marking content segments? 4. Semantic Markup: Are headings styled using semantic markup, e. | [
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37a72d8e-faf1-4c61-a0fd-309cea2cec70 | completed | d7bb4d65-95e8-42da-92af-39233f2112bd | These are the objectives you want to connect your KPIs to so you can accurately determine whether your SEO efforts are successful. | [
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d26e0f5e-47b6-452b-b19e-7c5e840ca685 | completed | 7ccbe905-1b48-4a1c-a18a-bf5b58294d9e | This course is comprised of 24 subjects, this includes 8 core subjects, 8 specialisation core subjects and 8 elective subjects. Please note, courses marked with * are an example only. | [
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5b136e3c-9f3e-4e6c-947a-87284940f481 | completed | 163d1484-e213-4e6e-96ae-f6454eaaa2b2 | Once you answer these questions, you can proceed with your marketing strategies. | [
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e17fafc5-2303-499a-900a-084093eaf91d | completed | 0ed93fed-0c45-43ad-ba95-f7fcd4f30d8b | This might additionally like to consider essays. Starting words to utilize in essays. | [
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2a37a7ac-e14d-45bf-9bc1-3845c7afc0f5 | completed | 568cb493-9981-4620-985f-63cb932d5e4a | We have been blessed with material assets. The Endowment Fund, the Invested Reserve Funds and Real Properties are a part of our life together and help equip us in fulfilling our mission and ministry. | [
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b15e5a99-1d48-49a6-a40a-9cba71d21165 | completed | 7ce974bb-edd3-4060-8a6c-e57841fe355a | Organizations get big by creating and sustaining a portfolio of revenue sources, likey over decades. | [
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0e53065b-8d52-4301-8439-12247e2f633e | completed | 31b1555b-8c87-4a15-8d0e-adaa97e53f1e | The markets continue to trade at or near all-time highs, so I'm continuing my focus on finding stocks that trade at or below fair value. | [
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331b0935-6463-40bf-a5a3-e95493e32cfd | completed | aeeae30f-6c90-4f0d-a2ea-d22927deb3f5 | Part 1 covered dividend increases of Energy, Financials, and Real Estate sector stocks. | [
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d55da535-fc7c-42d0-939c-f6c186a01842 | completed | 466912fd-0e16-4503-882e-37ec682b2784 | • Markets and Shopping Centers: Jalal Sons, Super Market, E-Block Market, Main Market of A Block of nearest society i. e Sui Gas Society. | [
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End of preview. Expand
in Data Studio
Dataset Card for argilla_dataset
This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.
Using this dataset with Argilla
To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:
import argilla as rg
ds = rg.Dataset.from_hub("sophie021/argilla_dataset", settings="auto")
This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.
Using this dataset with datasets
To load the records of this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code:
from datasets import load_dataset
ds = load_dataset("sophie021/argilla_dataset")
This will only load the records of the dataset, but not the Argilla settings.
Dataset Structure
This dataset repo contains:
- Dataset records in a format compatible with HuggingFace
datasets. These records will be loaded automatically when usingrg.Dataset.from_huband can be loaded independently using thedatasetslibrary viaload_dataset. - The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
- A dataset configuration folder conforming to the Argilla dataset format in
.argilla.
The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.
Fields
The fields are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.
| Field Name | Title | Type | Required | Markdown |
|---|---|---|---|---|
| text | Text | text | True | False |
Questions
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
|---|---|---|---|---|---|
| span_label | Classify individual tokens into given labels | span | True | N/A | N/A |
Data Instances
An example of a dataset instance in Argilla looks as follows:
{
"_server_id": "ba989482-6521-407f-8b55-c31b656044dc",
"fields": {
"text": "They cover all kind of instruments with Cryptocurrency just being the main focus of the company. Stocks: you get to enjoy access to trade on the potential movement of top stocks shares like US: BAC, MS, APPL; JP: Toyota, Sony, NTT; DE: BAY and ALD."
},
"id": "675de29d-9ed1-480a-83c2-63bf95867472",
"metadata": {},
"responses": {
"span_label": [
{
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]
},
"status": "completed",
"suggestions": {
"span_label": {
"agent": null,
"score": null,
"value": [
{
"end": 34,
"label": "asset",
"start": 23
},
{
"end": 54,
"label": "asset",
"start": 40
},
{
"end": 95,
"label": "organization",
"start": 88
},
{
"end": 103,
"label": "asset",
"start": 97
},
{
"end": 137,
"label": "transaction",
"start": 132
},
{
"end": 177,
"label": "asset",
"start": 171
},
{
"end": 201,
"label": "market index",
"start": 199
},
{
"end": 207,
"label": "market index",
"start": 203
},
{
"end": 247,
"label": "market index",
"start": 244
}
]
}
},
"vectors": {}
}
While the same record in HuggingFace datasets looks as follows:
{
"_server_id": "ba989482-6521-407f-8b55-c31b656044dc",
"id": "675de29d-9ed1-480a-83c2-63bf95867472",
"span_label.responses": [
[
{
"end": 34,
"label": "financial term",
"start": 23
},
{
"end": 54,
"label": "currency",
"start": 40
},
{
"end": 95,
"label": "organization",
"start": 88
},
{
"end": 103,
"label": "asset",
"start": 97
},
{
"end": 201,
"label": "market index",
"start": 199
},
{
"end": 207,
"label": "market index",
"start": 203
},
{
"end": 247,
"label": "market index",
"start": 244
},
{
"end": 239,
"label": "market index",
"start": 236
},
{
"end": 234,
"label": "market index",
"start": 232
},
{
"end": 230,
"label": "market index",
"start": 227
},
{
"end": 225,
"label": "market index",
"start": 221
},
{
"end": 219,
"label": "market index",
"start": 213
},
{
"end": 192,
"label": "location",
"start": 190
},
{
"end": 211,
"label": "location",
"start": 209
},
{
"end": 184,
"label": "asset",
"start": 171
}
]
],
"span_label.responses.status": [
"submitted"
],
"span_label.responses.users": [
"c9be73ea-b48a-4278-bd4c-99f4abf2c513"
],
"span_label.suggestion": [
{
"end": 34,
"label": "asset",
"start": 23
},
{
"end": 54,
"label": "asset",
"start": 40
},
{
"end": 95,
"label": "organization",
"start": 88
},
{
"end": 103,
"label": "asset",
"start": 97
},
{
"end": 137,
"label": "transaction",
"start": 132
},
{
"end": 177,
"label": "asset",
"start": 171
},
{
"end": 201,
"label": "market index",
"start": 199
},
{
"end": 207,
"label": "market index",
"start": 203
},
{
"end": 247,
"label": "market index",
"start": 244
}
],
"span_label.suggestion.agent": null,
"span_label.suggestion.score": null,
"status": "completed",
"text": "They cover all kind of instruments with Cryptocurrency just being the main focus of the company. Stocks: you get to enjoy access to trade on the potential movement of top stocks shares like US: BAC, MS, APPL; JP: Toyota, Sony, NTT; DE: BAY and ALD."
}
Data Splits
The dataset contains a single split, which is train.
Dataset Creation
Curation Rationale
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
Annotation guidelines
Classify individual tokens into given labels
Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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