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  2. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    The Hugging Face Hub is a platform (centralized web service) for hosting: [19] Git-based code repositories, including discussions and pull requests for projects. models, also with Git-based version control; datasets, mainly in text, images, and audio;

  3. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Text Classification 1999 [262] H. Begleiter P300 Interface Dataset Data from nine subjects collected using P300-based brain-computer interface for disabled subjects. Split into four sessions for each subject. MATLAB code given. 1,224 Text Classification 2008 [263] [264] U. Hoffman et al. Heart Disease Data Set

  4. BLOOM (language model) - Wikipedia

    en.wikipedia.org/wiki/BLOOM_(language_model)

    The model, as well as the code base and the data used to train it, are distributed under free licences. [3] BLOOM was trained on approximately 366 billion (1.6TB) tokens from March to July 2022. [4] [5] BLOOM is the main outcome of the BigScience collaborative initiative, [6] a one-year-long research workshop that took place between May 2021 ...

  5. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.

  6. Category:Hugging Face people - Wikipedia

    en.wikipedia.org/wiki/Category:Hugging_Face_people

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us

  7. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification, and sequence-to-sequence-based language generation tasks such as question answering and conversational response generation.

  8. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2).

  9. Concept-based image indexing - Wikipedia

    en.wikipedia.org/wiki/Concept-based_image_indexing

    Concept-based image indexing, also variably named as "description-based" or "text-based" image indexing/retrieval, refers to retrieval from text-based indexing of images that may employ keywords, subject headings, captions, or natural language text (Chen & Rasmussen, 1999).