5 models · ranked by HuggingFace downloads
by BAAI
BGE-Reranker-v2-M3 is BAAI's multilingual cross-encoder reranker built on XLM-RoBERTa, designed for re-ranking retrieved passages in multilingual RAG or search pipelines. It jointly encodes query-passage pairs to produce relevance scores, providing higher accuracy than bi-encoder similarity for the same candidate set. Apache 2.0 licensed with text-embeddings-inference support.
by ProsusAI
FinBERT is a BERT model fine-tuned on financial news and financial communications text for financial sentiment analysis, classifying text as positive, negative, or neutral from a finance domain perspective. Developed by Prosus AI (Naspers), it targets applications where general-purpose sentiment models fail on financial jargon and market-specific framing.
by ncbi
MedCPT-Cross-Encoder is a BERT-based cross-encoder from NCBI fine-tuned for medical text relevance scoring, trained on PubMed query-article pairs. It takes a query and a candidate passage and scores their relevance, making it a reranking component in medical information retrieval pipelines. The model is typically paired with MedCPT's query encoder for a full retrieval-reranking system.
by facebook
Meta's fastText-based language identification model, capable of identifying 176 languages from short text strings. Extremely fast CPU inference makes it practical for preprocessing pipelines that need to route text by language.
by FacebookAI
RoBERTa-large fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus, commonly used for zero-shot text classification via the NLI entailment trick. One of the most frequently used models for zero-shot classification before dedicated models like DeBERTa-MNLI improved further.