BART (language model)
BART (an acronym for Bidirectional and Auto-Regressive Transformers) is a transformer-based encoder-decoder language model introduced by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman…
Natural Language Processing
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Showing 1-24 of 24 articles
Bert-base-uncased model
PyTorch, TensorFlow, JAX, Rust, Core ML, Safetensors, Transformers
Conversational Models
Conversational models are computational systems designed to carry on a dialogue with human users in natural language
DistilBERT
DistilBERT is a compressed version of BERT released by Hugging Face in October 2019 that is 40% smaller and 60% faster than BERT-base while retaining 97% of its language-understanding performance on the GLUE…
ELMo (Embeddings from Language Models)
ELMo (Embeddings from Language Models) is a deep contextualized word embedding method, introduced in 2018 by the Allen Institute for AI (AI2) and the University of Washington
Fill-Mask Models
Fill-mask models are language models trained with a masked language modeling (MLM) objective, in which a fraction of the tokens in an input sequence are hidden behind a special [MASK] symbol and the model…
Jina Embeddings v3
Jina Embeddings v3 is a multilingual text embedding model released by Jina AI on September 18, 2024, with 570 million parameters, support for 89 languages, an 8,192 token context window, and a stack of…
Llama 3
Llama 3 is a family of open-weight large language models developed by Meta. Meta released the original Llama 3 checkpoints on April 18, 2024, in 8-billion-parameter and 70-billion-parameter sizes.
Question Answering Models
Question answering (QA) models are natural language processing systems that take a natural-language question as input and return a natural-language answer, optionally grounded in a supplied passage, document…
SciBERT
SciBERT is a BERT-based language model pretrained from scratch on a large corpus of scientific papers, built by the Allen Institute for AI (AI2).
Sentence Similarity Models
Sentence similarity models are machine learning systems that map natural language sentences to fixed-length numerical vectors (sentence embeddings) so that semantically related sentences sit close together in…
Sentence-BERT (SBERT)
Sentence-BERT (SBERT) is a modification of the pretrained BERT transformer network that produces semantically meaningful, fixed-size sentence embeddings comparable with simple cosine similarity.
Sentence-transformers/all-MiniLM-L6-v2 model
Sentence-transformers/all-MiniLM-L6-v2 model
Sentence-transformers/all-mpnet-base-v2 model
sentence-transformers/all-mpnet-base-v2
Summarization Models
Summarization models are natural language processing systems that condense a source document, set of documents, or dialogue into a shorter version that preserves the most important information.
Table Question Answering Models
Table question answering models (TableQA models) are machine learning systems that answer natural language questions over structured tabular data such as spreadsheets, database tables, and HTML tables…
Text Classification Models
Text classification models are machine learning systems that assign one or more predefined categorical labels to a span of natural language text, such as positive vs. negative, spam vs. ham
Text Generation Models
Text generation models are language models trained to produce coherent natural-language text by predicting tokens one at a time, each conditioned on the preceding context.
Text2Text Generation Models
Text-to-text (text2text) generation models are a family of neural network systems that frame many natural language processing tasks as a single problem: given an input text string
Token Classification Models
Token classification models are natural language processing systems that assign a discrete label to every token in an input sequence, where a token is typically a word, subword piece, or character.
Translation Models
Translation models are computational systems that convert text or speech from a source language into a target language.
Universal Speech Model
The Universal Speech Model (USM) is a family of large multilingual speech models developed by Google Research that performs automatic speech recognition (ASR) and speech-to-text translation across more than…
Voyage-3
Voyage-3 is a family of general-purpose text embedding models developed by Voyage AI, launched in September 2024 with voyage-3 and voyage-3-lite , expanded in January 2025 with voyage-3-large , and refreshed…
Zero-Shot Classification Models
Zero-shot classification models are machine learning systems that assign input text to a set of candidate categories without having seen labeled training examples for those specific categories.