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AI Models 84759

• OpenMed-NER-SpeciesDetect-BigMed-560M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species and organism names** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for species entity recognition - species and organism names. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated LINNAEUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-ZeroShot-NER-DNA-Small-166M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** Targets **molecular biology entities**: proteins, DNA/RNA, cell lines, and cell types in biomedical research content.Great for **pathway curation**, **molecular interaction mining**, and **omics-aware information extraction**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.

• tiny-random-ltx2.5↗

  optimum-intel-internal-testing

• distilbert-onnx↗

  philschmid

  This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7).

• Qwen3.6-27B-abliterated-NVFP4-GGUF↗

  prithivMLmods

• qwen3-8b-sft-RQ3↗

  priyamsahoo

• scb10x_-_llama-3-typhoon-v1.5x-8b-instruct-gguf↗

  RichardErkhov

• Qwen2.5-Coder-14B-Instruct-Uncensored-Q4_K_M-GGUF↗

  roleplaiapp

• Legal-BERTimbau-sts-base↗

  rufimelo

  --- language: - pt thumbnail: "Portugues BERT for the Legal Domain" pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers datasets: - assin - assin2 - rufimelo/PortugueseLegalSentences-v0 widget: - source_sentence: "O advogado apresentou as provas ao juíz." sentences: - "O juíz leu as provas." - "O juíz leu o recurso." - "O juíz atirou uma pedra." example_title: "Example 1" model-index: - name: BERTimbau results: - task: name: STS type: STS metrics: - name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.71457 - name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.73545 - name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.72383 --- This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-large which derives from BERTimbau base. It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. Using this model becomes easy when you have sentence-transformers installed: rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-largewhich derives from BERTimbau base. It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin and assin2 datasets.

• NeoHorse-1-4B-onw↗

  ryugyosoft