Feature Extraction

Embeddings optimized for semantic search and language understanding.

Second depth version of the NV embedding model.

Scalable model aimed at multilingual embeddings and applications.

Instruction-oriented model supporting multilingual tasks.

Focuses on embedding generation with enhanced performance.

Improved CLIP model designed for semantic context understanding.

Supports creative AI tasks to generate engaging text outputs.

Model designed for contextual understanding and enhancement.

Large BART model for advanced sequence-to-sequence tasks.

Focuses on embeddings creation for enhancing semantic understanding.

Compact model tailored for efficient language processing tasks.

A vector model for background analysis in textual data.

A smaller version of BGE for English text processing.

Specializes in user interaction and data mining tasks.

Tokenizer specifically designed for chemical SMILES data.

General-purpose Russian language model for sentence tasks.

A foundational Russian language representation model.

Multilingual model trained for English-Russian tasks.

A model designed for understanding UML diagrams in English.

Model specialized in code generation and explanation.

Longformer model for processing lengthy documents efficiently.

A compact model designed for Chinese tasks utilizing the BigBird architecture.

A combined model for understanding both text and visual elements.

A distilled version of M-BERT optimized for quick processing.

Cross-lingual model for generating semantically similar sentences.

German transformer for semantic text similarity tasks.

Chinese Longformer model for processing long documents.

Convolutional BERT variant optimized for processing tasks.

Portuguese distil BERT model for efficient language tasks.

Polish language model based on BERT architecture.

BERT model trained on UMLS for medical language applications.

SapBERT model specifically for understanding PubMed content.

Question answering model built around dense passage retrieval.

Monot5 model fine-tuned for effective query generation.

Language-agnostic model for English-Russian sentence embeddings.

Cite-oriented model for academic paper citation analysis.

Cite-oriented model for general academic text processing.

Versatile BioBERT model for various medical language tasks.

Model for enhancing retrieval performance in retrieval-augmented tasks.

Question encoder model designed for retrieval tasks.

Base model for robust speech recognition tasks.

Large HuBERT model designed for extensive speech recognition.

Korean BART model optimized for text generation tasks.

Model focused on efficient training for various NLP tasks.

Small canine variant optimized for performance in efficiency-driven tasks.

SciBERT model optimized for scientific natural language inference.

Base Indonesian BERT model for various textual tasks.

Large Indonesian BERT model optimized for diverse applications.

Utilizes multilingual BERT for semantic role labeling.

Designed for instant translation and understanding of different languages.

A model specialized for code understanding and generation.

Language model focused on understanding Chinese characters.

Utilizes speech data for robust learning tasks.

Large model focusing on advanced speech and audio understanding.

Ensures effective question answering for biomedical texts.

Comprehensive model for understanding Korean text.

Thai language model that supports various NLP applications.

Designed for intricate understanding of Hindi text.

Compact model for efficient natural language understanding.

An efficient model for speech recognition and understanding.

Focuses on processing and understanding Ancient Greek texts.

Provides supervised contrastive learning capabilities for improved representation.

Enhances representation learning through supervised methods with RoBERTa.

Specialized BERT model for processing Sanskrit texts.

Implements cross-language search capabilities focusing on Chinese.

Handles multilingual tasks with a focus on Russian inputs.

Enables large-scale understanding of Russian text.

Provides sentence embeddings optimized for distillation.

Matches paraphrased statements using MPNet.

Japanese sentence embeddings for enhanced understanding.

A BART-based model designed for the Vietnamese language.

Linguistically-informed BERT variant for low-resourced languages.

A model created for code understanding and generation tasks.

Bioinformatics model tailored for medical literature analysis.

Model designed for text analysis in Buddhism-related contexts.

CLIP model tailored for processing Japanese language inputs.

Korean sentence embeddings model optimized for semantic similarity.

HuBERT model for Mandarin audio processing and recognition.

Larger HuBERT model for advanced Mandarin speech tasks.

CLIP model for connecting visual and textual information in Chinese.

Generative model for producing lyrics and music based on input prompts.

DeepSpeed-inferred Bloom model for efficient text generation.

Model designed to be low-risk for diverse applications.

Encoder model for improving query relevance in searches.

RoBERTa model developed for enhancing English language tasks.

Multilingual model focusing on global language engagement.

A model focused on bioinformatics and healthcare text processing.

Embedding model focused on instructional tasks.

A lightweight language model for diverse NLP applications.

A model focusing on biomedical text analysis.

An all-in-one model designed for magical-themed content generation.

Trained for information retrieval and understanding across varied contexts.

Base variant of BART for various text generation tasks.