Brief-details: GPT-2-based lyrics generation model fine-tuned on Death Grips lyrics, offering text generation capabilities for creating Death Grips-style content.
Brief-details: Multilingual BERT model fine-tuned for Polish question-answering, achieving 60.67% EM and 71.89% F1 scores on Polish SQuAD1.1, trained on 39.5K Q&A pairs.
Brief-details: RBT4 is a 4-layer Chinese RoBERTa model with Whole Word Masking, developed by HFL. It's a lightweight variant optimized for Chinese NLP tasks, licensed under Apache-2.0.
Brief-details: RBT3 is a lightweight 3-layer Chinese RoBERTa model with whole word masking, designed for efficient NLP tasks. Built by HFL team, Apache-2.0 licensed.
Brief-details: Chinese ELECTRA base discriminator model for NLP tasks, developed by HFL. Efficient pre-trained model with competitive performance vs BERT, specifically optimized for Chinese language processing.
Brief-details: Chinese MacBERT-Large - Advanced BERT variant with MLM correction pre-training, optimized for Chinese NLP tasks. Features unique word masking and sentence ordering techniques.
Brief Details: Arabic Named Entity Recognition model based on BERT, capable of identifying 9 entity types including persons, organizations, and locations. 87% F-measure accuracy.
BRIEF DETAILS: Fine-tuned legal domain translation model for Spanish-Chinese language pair, based on Helsinki-NLP's opus-tatoeba. Trained on 9,972 legal document pairs.
Brief Details: TAPAS base model fine-tuned on WikiSQL for table question answering, featuring MLM pre-training and intermediate numerical reasoning capabilities.
BRIEF-DETAILS: SciBERT model fine-tuned on SNLI/MultiNLI for scientific text embeddings, achieving 74.50 STS score. Optimized for research paper analysis.
Brief-details: T5-efficient-mini is a deep-narrow variant of Google's T5 model with 31.23M parameters, optimized for downstream task performance through increased model depth.
Brief-details: Reformer language model trained on enwik8 Wikipedia data, specializing in character-level text generation using efficient Transformer architecture.
BRIEF DETAILS: PEGASUS-Newsroom is a state-of-the-art summarization model achieving 45.98/34.20/42.18 (R1/R2/RL) on the Newsroom dataset, trained on mixed C4 and HugeNews corpora.
BRIEF DETAILS: A powerful text summarization model by Google, specialized in multi-document summarization, featuring mixed & stochastic training on C4 and HugeNews datasets with 47.65 ROUGE-1 score.
Brief-details: Token-free multilingual T5 variant operating on raw UTF-8 bytes, supporting 102 languages. Ideal for noisy text processing and cross-lingual tasks.
Brief-details: Multilingual T5 model (XL variant) trained on mC4 dataset covering 101 languages. Supports text-to-text generation tasks. Apache 2.0 licensed.
BRIEF DETAILS: A specialized BigBird-Pegasus model fine-tuned for summarizing scientific papers, particularly PubMed articles. Features block sparse attention for handling long sequences up to 4096 tokens.
BRIEF DETAILS: BigBird-Pegasus model specialized for patent document summarization, featuring sparse-attention transformer architecture supporting 4096-length sequences
Brief Details: BigBird-Pegasus model specialized for scientific paper summarization, featuring block sparse attention for handling long sequences up to 4096 tokens.
Brief Details: KoBART-base-v2: A 124M-parameter Korean BART model trained on 40GB+ text data. Features text infilling and encoder-decoder architecture optimized for Korean language tasks.
Brief Details: Korean BART-based text summarization model with 124M parameters. Specializes in Korean news summarization with MIT license.
