Brief-details: State-of-the-art text embedding model with 434M parameters, supporting 8192 token context length and achieving 65.39 MTEB score. Built on transformer++ architecture.
Brief Details: Multilingual sentiment analysis model based on XLM-RoBERTa, achieving 69.3% accuracy across languages for tweet classification
Brief-details: SDXL 1.0 Base - Advanced text-to-image diffusion model from Stability AI. Features dual text encoders and improved generation quality over SD 1.5/2.1
Brief-details: CamemBERT is a powerful French language model based on RoBERTa architecture with 110M parameters, trained on OSCAR dataset for masked language modeling tasks.
Brief-details: Base-sized English embedding model (109M params) optimized for retrieval and semantic search, achieving strong MTEB benchmark performance across tasks like clustering and reranking.
Brief-details: DistilRoBERTa-base is a lightweight, distilled version of RoBERTa with 82.8M parameters, offering 2x faster performance while maintaining strong language understanding capabilities.
Brief-details: DistilGPT2 is a compressed version of GPT-2 with 82M parameters, trained via knowledge distillation for faster, lighter text generation while maintaining strong performance.
Brief Details: InfoXLM-Large: Microsoft's cross-lingual language model using information-theoretic framework. 2.8M+ downloads, popular for multilingual NLP tasks.
Brief-details: State-of-the-art English language embedding model with 335M parameters, achieving top performance on MTEB benchmarks. Optimized for retrieval and similarity tasks.
Brief Details: A DETR-based transformer model with 28.8M parameters for table detection in documents, trained on PubTables1M dataset with MIT license.
Brief Details: Indonesian RoBERTa-based POS tagger achieving 96.25% accuracy on IndoNLU dataset. 124M params, MIT licensed, optimized for Indonesian text.
Brief Details: DistilHuBERT - Efficient speech representation model with 23.5M params. 75% smaller than HuBERT while maintaining performance. Ideal for academic/small-scale ML.
Brief Details: SigLIP vision-language model with 878M parameters optimized for zero-shot classification. Uses sigmoid loss, trained on WebLI dataset at 384x384 resolution.
Brief Details: BART-Large-MNLI is a 407M parameter NLI model fine-tuned for zero-shot classification, offering powerful multi-label text classification capabilities.
Brief-details: BGE-M3 is a versatile multilingual embedding model supporting dense retrieval, lexical matching, and multi-vector interaction across 100+ languages with 8192 token context.
BRIEF DETAILS: A compact yet powerful ColBERT-based retrieval model with 33.4M parameters, outperforming larger models in passage retrieval tasks while maintaining efficiency.
Brief Details: Audio Spectrogram Transformer with 86.6M params, fine-tuned on AudioSet. Converts audio to spectrograms for classification using ViT architecture.
Brief Details: BART base model (139M params) by Facebook - A transformer-based seq2seq model for text generation and comprehension tasks, pre-trained on English text.
Brief Details: BERTimbau Base - A state-of-the-art BERT model for Brazilian Portuguese with 110M parameters, trained on brWaC dataset for NLP tasks.
Brief-details: State-of-the-art multilingual speech recognition model with 1.54B parameters, supporting 99 languages and offering improved accuracy over previous versions.
Brief-details: Multilingual XLM-RoBERTa model (560M params) fine-tuned for token classification/NER, supporting 94 languages with strong performance on English CoNLL-2003 dataset.
