Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
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
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
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/