.. hint::

   **Quickstart:** Find `curated datasets <https://huggingface.co/collections/sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552>`_ or `community datasets <https://huggingface.co/datasets?other=sentence-transformers>`_, choose a loss function via this `loss overview <loss_overview.html>`_, and `verify <training_overview.html#dataset-format>`_ that it works with your dataset.

.. tip::

   You can use :func:`~sentence_transformers.util.hard_negatives.mine_hard_negatives` to convert a dataset of positive pairs into a dataset of triplets. It uses a :class:`~sentence_transformers.sentence_transformer.model.SentenceTransformer` model to find hard negatives: texts that are similar to the first dataset column, but are not quite as similar as the text in the second dataset column. Datasets with hard triplets often outperform datasets with just positive pairs.
   
   For example, we mined hard negatives from `sentence-transformers/gooaq <https://huggingface.co/datasets/sentence-transformers/gooaq>`_ to produce `tomaarsen/gooaq-hard-negatives <https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives>`_ and trained `tomaarsen/mpnet-base-gooaq <https://huggingface.co/tomaarsen/mpnet-base-gooaq>`_ and `tomaarsen/mpnet-base-gooaq-hard-negatives <https://huggingface.co/tomaarsen/mpnet-base-gooaq-hard-negatives>`_ on the two datasets, respectively. Sadly, the two models use a different evaluation split, so their performance can't be compared directly.

.. tip::

   Multimodal models require additional dependencies. Install them with e.g. ``pip install -U "sentence-transformers[image]"`` for image support. See `Installation <../installation.html>`_ for all options.

The following input types are supported:

- **Text**: strings.
- **Image**: PIL images, file paths, URLs, or numpy/torch arrays.
- **Audio**: file paths, numpy/torch arrays, dicts with ``"array"`` and ``"sampling_rate"`` keys, or (if ``torchcodec`` installed) :class:`torchcodec.AudioDecoder <torchcodec.decoders.AudioDecoder>` instances.
- **Video**: file paths, numpy/torch arrays, dicts with ``"array"`` and ``"video_metadata"`` keys, or (if ``torchcodec`` installed) :class:`torchcodec.VideoDecoder <torchcodec.decoders.VideoDecoder>` instances.
- **Multimodal dicts**: a dict mapping modality names to values, e.g. ``{"text": ..., "audio": ...}``. The keys must be ``"text"``, ``"image"``, ``"audio"``, or ``"video"``.
- **Chat messages**: a list of dicts with ``"role"`` and ``"content"`` keys for multimodal models that use an uncommon chat template to combine text and non-text inputs.

from datasets import load_dataset

dataset = load_dataset("tomaarsen/llamaindex-vdr-en-train-preprocessed", "train", split="train")
"""
Dataset({
    features: ['query', 'image', 'negative_0', 'negative_1', 'negative_2', 'negative_3'],
    num_rows: 10000
})
"""
print(dataset[0]["query"])
# "What are the new anthropological perspectives on development as discussed by Quarles Van Ufford and Giri in 2003?"

print(dataset[0]["image"])
# <PIL.Image.Image image mode=RGB size=...>

The `Datasets library <https://huggingface.co/docs/datasets/index>`_ (``pip install datasets``) allows you to load datasets from the Hugging Face Hub with the :func:`~datasets.load_dataset` function::

   from datasets import load_dataset

   # Indicate the dataset id from the Hub
   dataset_id = "sentence-transformers/natural-questions"
   dataset = load_dataset(dataset_id, split="train")
   """
   Dataset({
      features: ['query', 'answer'],
      num_rows: 100231
   })
   """
   print(dataset[0])
   """
   {
      'query': 'when did richmond last play in a preliminary final',
      'answer': "Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next."
   }
   """

.. tip::
   
   It's common for Hugging Face Datasets to contain extraneous columns, e.g. sample_id, metadata, source, type, etc. You can use :meth:`Dataset.remove_columns <datasets.Dataset.remove_columns>` to remove these columns, as they will be used as inputs otherwise. You can also use :meth:`Dataset.select_columns <datasets.Dataset.select_columns>` to keep only the desired columns.

.. note::

   We advise users to tag datasets that can be used for training embedding models with ``sentence-transformers`` by adding ``tags: sentence-transformers``. We would also gladly accept high quality datasets to be added to the list above for all to see and use.