Upload folder using huggingface_hub
Browse files- .argilla/dataset.json +16 -0
- .argilla/settings.json +69 -0
- .argilla/version.json +3 -0
- README.md +152 -37
.argilla/dataset.json
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{
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"id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"name": "dataset-0",
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"guidelines": null,
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"allow_extra_metadata": true,
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"status": "ready",
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"distribution": {
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"strategy": "overlap",
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"min_submitted": 1
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},
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"metadata": null,
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"workspace_id": "f03b7510-be77-4064-b513-b73036dfe6c1",
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"last_activity_at": "2024-12-13T15:59:08.871254",
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"inserted_at": "2024-12-13T15:59:08.871254",
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"updated_at": "2024-12-13T15:59:08.871254"
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}
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.argilla/settings.json
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{
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"guidelines": null,
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"allow_extra_metadata": true,
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"distribution": {
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"strategy": "overlap",
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"min_submitted": 1
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},
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"fields": [
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{
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"id": "806c4fb1-576f-4cf7-ad5a-4fca63083c0b",
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"name": "text",
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"title": "Field Title",
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"required": false,
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"settings": {
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"type": "text",
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"use_markdown": false
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},
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"dataset_id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"inserted_at": "2024-12-13T15:59:08.871829",
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"updated_at": "2024-12-13T15:59:08.871829"
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}
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],
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"questions": [
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{
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"id": "2d384f22-3471-4773-a665-144c6be872b4",
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"name": "text-question",
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"title": "Question Title",
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"description": "Question Description",
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"required": true,
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"settings": {
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"type": "text",
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"use_markdown": false
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},
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"dataset_id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"inserted_at": "2024-12-13T15:59:08.872343",
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"updated_at": "2024-12-13T15:59:08.872343"
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}
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],
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"metadata": [],
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"vectors": [
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{
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"id": "b088ffcb-3e6a-4f4d-998d-3180c36ef96d",
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"name": "vector-a",
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"title": "Vector A",
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"dimensions": 3,
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"dataset_id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"inserted_at": "2024-12-13T15:59:08.873434",
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"updated_at": "2024-12-13T15:59:08.873434"
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},
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{
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"id": "f3088060-d1d7-4cc3-a3c9-675eb212c50f",
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"name": "vector-b",
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"title": "Vector B",
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"dimensions": 2,
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"dataset_id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"inserted_at": "2024-12-13T15:59:08.873740",
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"updated_at": "2024-12-13T15:59:08.873740"
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},
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{
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"id": "e54aa34e-3cf4-4a3e-b695-d2d3db65d541",
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"name": "vector-c",
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"title": "Vector C",
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"dimensions": 4,
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"dataset_id": "854620da-9f48-4ffd-8739-b34a3f5efaeb",
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"inserted_at": "2024-12-13T15:59:08.873967",
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"updated_at": "2024-12-13T15:59:08.873967"
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}
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]
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}
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.argilla/version.json
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{
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"argilla": "2.6.0dev0"
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}
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README.md
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---
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-
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- name: status
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dtype: string
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- name: inserted_at
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dtype: timestamp[us]
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- name: updated_at
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dtype: timestamp[us]
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- name: _server_id
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dtype: string
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- name: text
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dtype: string
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- name: text-question.responses
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dtype: 'null'
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- name: text-question.responses.users
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dtype: 'null'
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- name: text-question.responses.status
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dtype: 'null'
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- name: vector.vector-a
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sequence: float64
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- name: vector.vector-b
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sequence: float64
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- name: vector.vector-c
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dtype: 'null'
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splits:
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- name: train
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num_bytes: 147
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num_examples: 1
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download_size: 5646
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dataset_size: 147
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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---
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tags:
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- rlfh
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- argilla
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- human-feedback
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---
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# Dataset Card for argilla-server-dataset-test-8d086fd7-6717-46b2-8845-13fc79819c60
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This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
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## Using this dataset with Argilla
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To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
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```python
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import argilla as rg
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ds = rg.Dataset.from_hub("argilla-internal-testing/argilla-server-dataset-test-8d086fd7-6717-46b2-8845-13fc79819c60", settings="auto")
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```
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This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.
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## Using this dataset with `datasets`
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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:
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```python
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from datasets import load_dataset
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ds = load_dataset("argilla-internal-testing/argilla-server-dataset-test-8d086fd7-6717-46b2-8845-13fc79819c60")
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```
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This will only load the records of the dataset, but not the Argilla settings.
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## Dataset Structure
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This dataset repo contains:
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* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`.
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* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
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* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.
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The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
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### Fields
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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.
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| Field Name | Title | Type | Required |
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| ---------- | ----- | ---- | -------- |
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| text | Field Title | text | False |
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### Questions
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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.
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| Question Name | Title | Type | Required | Description | Values/Labels |
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| ------------- | ----- | ---- | -------- | ----------- | ------------- |
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| text-question | Question Title | text | True | Question Description | N/A |
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<!-- check length of metadata properties -->
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### Vectors
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The **vectors** contain a vector representation of the record that can be used in search.
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| Vector Name | Title | Dimensions |
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|-------------|-------|------------|
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| vector-a | Vector A | [1, 3] |
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| vector-b | Vector B | [1, 2] |
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| vector-c | Vector C | [1, 4] |
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### Data Splits
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The dataset contains a single split, which is `train`.
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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#### Annotation guidelines
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[More Information Needed]
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[More Information Needed]
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### Citation Information
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[More Information Needed]
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### Contributions
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[More Information Needed]
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