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features
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Description

An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.

Description

Pinecone Rerank V0 is a cross-encoder model specifically designed to enhance precision in reranking tasks, thereby improving enterprise search and retrieval-augmented generation (RAG) systems. This model processes both queries and documents simultaneously, enabling it to assess fine-grained relevance and assign a relevance score ranging from 0 to 1 for each query-document pair. With a maximum context length of 512 tokens, it ensures that the quality of ranking is maintained. In evaluations based on the BEIR benchmark, Pinecone Rerank V0 stood out by achieving the highest average NDCG@10, surpassing other competing models in 6 out of 12 datasets. Notably, it achieved an impressive 60% increase in performance on the Fever dataset when compared to Google Semantic Ranker, along with over 40% improvement on the Climate-Fever dataset against alternatives like cohere-v3-multilingual and voyageai-rerank-2. Accessible via Pinecone Inference, this model is currently available to all users in a public preview, allowing for broader experimentation and feedback. Its design reflects an ongoing commitment to innovation in search technology, making it a valuable tool for organizations seeking to enhance their information retrieval capabilities.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) No 
Cohere No 
Confluent No 
Context Data No 
Databricks No 
Datavolo No 
Fleak No 
GitHub Copilot No 
Google Cloud Platform No 
Haystack No 
LangChain No 
Langtrace No 
Llama No 
Microsoft Azure No 
Mimasa AI No 
Nexla No 
Nuclia No 
Pinecone No 
TwelveLabs No 
Vercel No 

Integrations

Amazon Web Services (AWS) Yes 
Cohere Yes 
Confluent Yes 
Context Data Yes 
Databricks Yes 
Datavolo Yes 
Fleak Yes 
GitHub Copilot Yes 
Google Cloud Platform Yes 
Haystack Yes 
LangChain Yes 
Langtrace Yes 
Llama Yes 
Microsoft Azure Yes 
Mimasa AI Yes 
Nexla Yes 
Nuclia Yes 
Pinecone Yes 
TwelveLabs Yes 
Vercel Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

$25 per month
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Apple

Founded

1976

Country

United States

Website

github.com/apple/ml-ferret

Vendor Details

Company Name

Pinecone

Founded

2019

Country

United States

Website

www.pinecone.io/blog/pinecone-rerank-v0-announcement/

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