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- Different Techniques for Sentence Semantic Similarity in NLP
Semantic similarity is the similarity between two words or two sentences phrase text It measures how close or how different the two pieces of word or text are in terms of their meaning and context In this article, we will focus on how the semantic similarity between two sentences is derived We will cover the following most used models Dov2Vec - An extension of word2vec SBERT - Transformer
- Advanced NLP Configurations - Kore. ai Documentation v8. 1
Any intent with a fuzzy match score of 95 or higher (on a scale of 0-100) is identified as a definitive match However, fuzzy matching can produce false positives when there are words with similar spellings but different meanings For example, possible vs impossible or available vs unavailable This behavior is problematic in some cases
- [1512. 05193] ABCNN: Attention-Based Convolutional Neural . . . - ar5iv
This work presents the ABCNN, an attention-based convolutional neural network, that has a powerful mechanism for modeling a sentence pair by taking into account the interdependence between the two sentences The ABCNN is a general architecture that can handle a wide variety of sentence pair modeling tasks
- AI Chatbot Module [Open-Sourced] returns Result,score,weight . . . - Roblox
When the bot doesn’t know the answer to the question you could return a random entry from its database The benefit to using this approach is it is all locally done and it relieves any API request you would do using OpenAI or other services
- ai-agents-backend llms-full. txt at main - GitHub
The final score is the average of these matches, representing the accuracy of the tool usage trajectory * `response_match_score`: This metric compares the agent's final natural language response to the expected final response, stored in the `reference` field
- Explaining Natural Language query results - The VLDB Journal
Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries We build upon this work, but focus on presenting a highly detailed form of the answers in NL The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations We develop a novel method for
- TeichAI gemini-2. 5-flash-11000x · Datasets at Hugging Face
TeichAI gemini-2 5-flash-11000x · Datasets at Hugging Facetrain · 11 1k rows
- RAG Techniques - OpenAI API + Qdrant
Overview: You can enhance your minimal Retrieval-Augmented Generation pipeline (OpenAI API + Qdrant vector DB) by incrementally adding various RAG techniques Below is a structured guide covering each technique, with explanations tailored to your setup, simple implementation ideas, and notes on trade-offs The focus is on lightweight, practical approaches (no heavy orchestration frameworks or
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