Model: Nuf-hugginface/modernbert-embed-quickb
Author: Nuf-hugginface
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, modernbert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:127, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:nomic-ai/modernbert-embed-base, base_model:finetune:nomic-ai/modernbert-embed-base, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: nomic-ai/modernbert-embed-base
Card metadata: language: en
license: apache-2.0
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:127, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget: source_sentence: What is the difference between traditional programming and ML?
sentences: Over the past few years, the field of ML has advanced rapidly, especially in the area of Natural Language Processing (NLP)—the ability of machines to understand and generate human language. At the forefront of this progress are Large Language Models (LLMs), such as OpenAI’s GPT (Generative Pre-trained Transformer), Google’s PaLM, and Meta’s LLaMA, . For example, integrating an LLM into a customer support chatbot might involve connecting it to a company’s internal knowledge base, enabling it to answer customer questions using accurate, up-to-date information., A major subset of AI is Machine Learning (ML), which involves algorithms that learn from data rather than being explicitly programmed. Instead of writing detailed instructions for every task, ML models find patterns in large datasets and use these patterns to make predictions or decisions, source_sentence: What is one of the tasks mentioned that involves creating new written content?
sentences: In summary, AI and ML form the foundation for intelligent automation, while LLMs represent a breakthrough in language understanding and generation. Integrating these models into real-world systems unlocks practical value, turning raw intelligence into tangible solutions, 8. Security and Compliance Integrations
Some organizations are integrating LLMs to detect anomalies in text communications (e.g., phishing detection or policy violations). LLMs can analyze language usage and flag potentially suspicious behavior more flexibly than keyword-based filters.

Challenges in LLM Integration
Despite their promise, integrating LLMs comes with challenges:, . These include text generation, summarization, translation, question answering, code generation, and more., source_sentence: What is one of the components mentioned alongside AI?
sentences: 2. Search Engines and Semantic Search
Traditional keyword-based search systems are being enhanced or replaced by semantic search, where LLMs understand the meaning behind queries. Instead of just matching words, they interpret intent., For example, e-commerce websites can deploy LLM-powered assistants to help customers find products, track orders, or get personalized recommendations—much more effectively than traditional rule-based bots., Introduction to AI, Machine Learning, LLMs, and Their Integration, source_sentence: What is required to provide intelligent features within broader applications?
sentences: . For instance, a spam filter doesn’t just block emails with specific keywords—it learns from thousands of examples what spam typically looks like., The Rise of LLM Integrations
While LLMs are powerful on their own, their true potential is unlocked through integration—connecting these models with other software, services, or systems to provide intelligent features within broader applications.

Here are some key ways LLMs are being integrated into the digital world:, For instance, in a document management system, a user might type "policies about sick leave", and the system—integrated with an LLM—could retrieve documents discussing "medical leave", "employee absence", and "illness policies", even if those exact words weren’t used., source_sentence: What type of dialogues can LLMs simulate?
sentences: Companies are also experimenting with Retrieval-Augmented Generation (RAG)—a technique where LLMs are paired with document databases (e.g., vector stores like Supabase, Pinecone, or Weaviate) to answer questions with enterprise-specific knowledge., . For example, integrating an LLM into a customer support chatbot might involve connecting it to a company’s internal knowledge base, enabling it to answer customer questions using accurate, up-to-date information., 5. Education and Learning Platforms
Educational tools like Khanmigo (from Khan Academy) and other tutoring platforms are leveraging LLMs to provide real-time help to students. LLMs can break down complex topics, provide feedback on writing, and simulate Socratic-style dialogues.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.6666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.6666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2666666666666667
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000007
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000003
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.6666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8310827786456928
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.7766666666666667
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.7766666666666667
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.6666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.8666666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.6666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2666666666666667
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.17333333333333337
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000003
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.6666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8666666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8203966331432972
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.7651851851851852
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.7651851851851852
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.6666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8666666666666667
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.6666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.28888888888888886
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000007
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000003
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.6666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8666666666666667
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8357043414408
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.7822222222222223
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.7822222222222223
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.5333333333333333
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.7333333333333333
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.8
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9333333333333333
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.5333333333333333
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2444444444444445
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.16000000000000003
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09333333333333335
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.5333333333333333
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.7333333333333333
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.7203966331432973
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.6540740740740741
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.6592022792022793
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.4666666666666667
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.6666666666666666
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.8
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8666666666666667
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.4666666666666667
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.22222222222222224
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.16000000000000003
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08666666666666668
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.4666666666666667
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.6666666666666666
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.6507228370099043
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.5822222222222223
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.58890559732665
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 7
Created: 2025-04-25 11:25:01