The request names OpenMed-ZeroShot-NER-Species-XLarge-770M, a specific model, but none of the candidate sources is that model's own page. Its listed relatives are the Hugging Face hub, which hosts zero-shot classification and token/NER-style models 4, and Replicate, which runs open-source models through a cloud API 2. Sentence Transformers is included for zero-shot embedding workflows 7. Please confirm which platform you plan to run the model on before choosing an interface.
A question back: Which platform or runtime do you want to run OpenMed-ZeroShot-NER-Species-XLarge-770M on (for example Hugging Face, a cloud API like Replicate, or a local Python environment)?
- 1
Hugging Facehuggingface.coverified and signed
Hosts open models and offers a Text Classification task that includes zero-shot classification, with its hub and huggingface-hub SDK as official access doors 4.
official:MCP server · @huggingface/mcp-clientSDK · huggingface-hubCLI · text-embeddings-inferencerepository · pwc-cliskill · hf-cli+23 community tools built for it
for agents: text_classification · document_question_answering · nlp
- 2
Replicatereplicate.comverified and signed
Runs open-source machine learning models through a cloud API and client library, with zero-shot classifier models among those offered 2.
+14 community tools built for it
for agents: georgedavila/bart-large-mnli-classifier · cjwbw/pix2pix-zero · shefa/turbo-enigma
- 3
SentenceTransformers Documentationsbert.netverified and signed
A Python SDK for using and training embedding and reranker models, which can be used for zero-shot tasks built on embeddings 7.
official:SDK · sentence-transformers+24 community tools built for it
for agents: XLNet · BERT · NLP
Sources
- 2Replicate - Run AI with an APIreplicate.com · read 2026-10-29
- 4Hugging Face – The AI community building the future.huggingface.co · read 2026-10-29
- 7SentenceTransformers Documentation — Sentence Transformers documentationsbert.net · read 2026-10-29
For agents: the 10 actions behind this answer
extract-structured-data · OpenAI (Activepieces) verified and signed
Extract Structured Data — Pull the fields you define out of a block of text.
georgedavila/bart-large-mnli-classifier · georgedavila/bart-large-mnli-classifier verified and signed
Zero-shot classifier which classifies text into categories of your choosing. Returns a dictionary of the most likely class and all class likelihoods.
create-specification · create-specification verified and signed
Create a new specification file for the solution, optimized for Generative AI consumption.
text_classification · Hugging Face (Activepieces) verified and signed
Text Classification — Classify text into categories using Hugging Face models - includes zero-shot classification for custom categories
Entity · dotnet-ef verified and signed
neo4j-document-import-skill · neo4j-document-import-skill verified and signed
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity g
XLNet · sentence-transformers verified and signed
annotations · typing-extensions verified and signed
python · fastcore verified and signed
tao-finetune-clip · tao-finetune-clip verified and signed
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT. This is a single-action
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.