sentence-transformers/all-MiniLM-L6-v2
Sentence Similarity
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Datasets:
Tasks:
Sentence Similarity
Sub-tasks:
semantic-similarity-classification
Languages:
English
Tags:
License:
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The task_categories "paraphrase-mining" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Dataset Card for "simple-wiki"
Dataset Summary
This dataset contains pairs of equivalent sentences obtained from Wikipedia.
Supported Tasks
- Sentence Transformers training; useful for semantic search and sentence similarity.
Languages
- English.
Dataset Structure
Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar sentences.
Usage Example
Install the 🤗 Datasets library with pip install datasets and load the dataset from the Hub with:
from datasets import load_dataset
dataset = load_dataset("embedding-data/simple-wiki")
The dataset is loaded as a DatasetDict and has the format:
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 102225
})
})
Review an example i with:
dataset["train"][i]["set"]
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
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