Datasets:
size_categories: 1K<n<10K
tags:
- argilla
task_categories:
- token-classification
language:
- en
dev-ner-ontonotes
Validation set of NER dataset OntoNotes5 created with Argilla for a Argilla Meetup talk.
Usage
Load 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.FeedbackDataset.from_huggingface("louisguitton/dev-ner-ontonotes")
Load with datasets
To load 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("louisguitton/dev-ner-ontonotes")
Dataset Structure
Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
|---|---|---|---|---|
| text | Text | text | True | False |
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 |
|---|---|---|---|---|---|
| entities | Highlight the entities in the text: | span | True | N/A | N/A |
The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the metadata_properties defined in the dataset configuration file in argilla.yaml.
| Metadata Name | Title | Type | Values | Visible for Annotators |
|---|
The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
Data Instances
An example of a dataset instance in Argilla looks as follows:
{
"external_id": null,
"fields": {
"text": "A Russian diver has found the bodies of three of the 118 sailors who were killed when the nuclear submarine Kursk sank in the Barents Sea ."
},
"metadata": {},
"responses": [],
"suggestions": [
{
"agent": "gold_labels",
"question_name": "entities",
"score": null,
"type": null,
"value": [
{
"end": 9,
"label": "NORP",
"score": 1.0,
"start": 2
},
{
"end": 45,
"label": "CARDINAL",
"score": 1.0,
"start": 40
},
{
"end": 56,
"label": "CARDINAL",
"score": 1.0,
"start": 53
},
{
"end": 113,
"label": "PRODUCT",
"score": 1.0,
"start": 108
},
{
"end": 137,
"label": "LOC",
"score": 1.0,
"start": 122
}
]
}
],
"vectors": {}
}
While the same record in HuggingFace datasets looks as follows:
{
"entities": [],
"entities-suggestion": {
"end": [
9,
45,
56,
113,
137
],
"label": [
"NORP",
"CARDINAL",
"CARDINAL",
"PRODUCT",
"LOC"
],
"score": [
1.0,
1.0,
1.0,
1.0,
1.0
],
"start": [
2,
40,
53,
108,
122
],
"text": [
"Russian",
"three",
"118",
"Kursk",
"the Barents Sea"
]
},
"entities-suggestion-metadata": {
"agent": "gold_labels",
"score": null,
"type": null
},
"external_id": null,
"metadata": "{}",
"text": "A Russian diver has found the bodies of three of the 118 sailors who were killed when the nuclear submarine Kursk sank in the Barents Sea ."
}
Data Fields
Among the dataset fields, we differentiate between the following:
Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
- text is of type
text.
- text is of type
Questions: These are the questions that will be asked to the annotators. They can be of different types, such as
RatingQuestion,TextQuestion,LabelQuestion,MultiLabelQuestion, andRankingQuestion.- entities is of type
span.
- entities is of type
Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
- (optional) entities-suggestion is of type
span.
- (optional) entities-suggestion is of type
Additionally, we also have two more fields that are optional and are the following:
- metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the
metadata_propertiesdefined in the dataset configuration file inargilla.yaml. - external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
Data Splits
The dataset contains a single split, which is validation.