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Upload folder using huggingface_hub

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  1. .argilla/dataset.json +16 -0
  2. .argilla/settings.json +53 -0
  3. .argilla/version.json +3 -0
  4. README.md +142 -31
.argilla/dataset.json ADDED
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+ {
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+ "id": "3b3dbd2b-7ad3-4ec1-a8a2-730a254b6c42",
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+ "name": "sentiment_challenge1",
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+ "guidelines": "label each movie review as 'positive' or 'negative' based on sentiment.",
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+ "allow_extra_metadata": false,
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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": "068acf81-6271-46b4-bf5d-e7d7d501cd2c",
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+ "last_activity_at": "2025-09-24T16:41:02.824661",
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+ "inserted_at": "2025-09-24T15:36:56.964194",
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+ "updated_at": "2025-09-24T15:36:57.376787"
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+ }
.argilla/settings.json ADDED
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+ {
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+ "guidelines": "label each movie review as 'positive' or 'negative' based on sentiment.",
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+ "allow_extra_metadata": false,
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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": "69896ef1-bf29-4d7d-b729-a7d75dc8c5de",
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+ "name": "review",
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+ "title": "review",
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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": "3b3dbd2b-7ad3-4ec1-a8a2-730a254b6c42",
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+ "inserted_at": "2025-09-24T15:36:57.184979",
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+ "updated_at": "2025-09-24T15:36:57.184979"
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+ }
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+ ],
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+ "questions": [
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+ {
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+ "id": "e1d3bc94-ef64-4758-945d-e7d1c8ae892f",
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+ "name": "sentiment",
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+ "title": "sentiment",
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+ "description": null,
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+ "required": true,
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+ "settings": {
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+ "type": "label_selection",
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+ "options": [
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+ {
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+ "value": "positive",
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+ "text": "positive",
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+ "description": null
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+ },
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+ {
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+ "value": "negative",
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+ "text": "negative",
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+ "description": null
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+ }
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+ ],
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+ "visible_options": null
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+ },
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+ "dataset_id": "3b3dbd2b-7ad3-4ec1-a8a2-730a254b6c42",
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+ "inserted_at": "2025-09-24T15:36:57.232440",
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+ "updated_at": "2025-09-24T15:36:57.232440"
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+ }
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+ ],
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+ "metadata": [],
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+ "vectors": []
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+ }
.argilla/version.json ADDED
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+ {
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+ "argilla": "2.8.0"
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+ }
README.md CHANGED
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  ---
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- dataset_info:
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- features:
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- - name: id
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- dtype: string
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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: review
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- dtype: string
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- - name: sentiment.responses
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- sequence: string
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- - name: sentiment.responses.users
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- sequence: string
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- - name: sentiment.responses.status
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- sequence: string
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- splits:
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- - name: train
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- num_bytes: 163373
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- num_examples: 100
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- download_size: 110598
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- dataset_size: 163373
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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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+
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+ # Dataset Card for sentiment_challenge1
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+
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+
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+
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+
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+
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+
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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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+
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+
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+ ## Using this dataset with Argilla
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+
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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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+
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+ ```python
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+ import argilla as rg
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+
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+ ds = rg.Dataset.from_hub("remasdeefallah1/sentiment_challenge1", settings="auto")
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+ ```
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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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+
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+ ## Using this dataset with `datasets`
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+
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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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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("remasdeefallah1/sentiment_challenge1")
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+ ```
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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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+
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+ ## Dataset Structure
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+
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+ This dataset repo contains:
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+
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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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+
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+ The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
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+
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+ ### Fields
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+
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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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+
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+ | Field Name | Title | Type | Required |
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+ | ---------- | ----- | ---- | -------- |
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+ | review | review | text | True |
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+
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+
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+ ### Questions
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+
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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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+
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+ | Question Name | Title | Type | Required | Description | Values/Labels |
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+ | ------------- | ----- | ---- | -------- | ----------- | ------------- |
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+ | sentiment | sentiment | label_selection | True | N/A | ['positive', 'negative'] |
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+
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+
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+ <!-- check length of metadata properties -->
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+
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+
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+
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+
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+ ### Data Splits
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+
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+ The dataset contains a single split, which is `train`.
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation guidelines
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+
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+ label each movie review as 'positive' or 'negative' based on sentiment.
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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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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+
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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]