sentence-transformers/all-MiniLM-L6-v2
Sentence Similarity • 22.7M • Updated • 254M • • 5.07k
Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
json
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
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Unexpected token '<', "<html>
<h"... is not valid JSON
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YAML Metadata Error:"paperswithcode_id" with value "embedding-data/SPECTER" fails to match the required pattern: /^[^/.]*$/
YAML Metadata Warning: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, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, 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-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, 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, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Dataset Card for "SPECTER"
Dataset Summary
Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.
Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):
{"set": [anchor, positive, negative]}
{"set": [anchor, positive, negative]}
...
{"set": [anchor, positive, negative]}
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.
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/SPECTER")
The dataset is loaded as a DatasetDict and has the format:
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 684100
})
})
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
Citation Information
Contributions
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