This presentation was provided by William Mattingly of the Smithsonian Institution, during the seventh segment of the NISO training series "AI & Prompt Design." Session 7: Open Source Language Models, was held on May 16, 2024.
ExperTwin: An Alter Ego in Cyberspace for Knowledge WorkersCarlos Toxtli
ExperTwin is a Knowledge Advantage Machine (KAM) that is able to collect data from your areas of interest and present it in-time, in-context and in place to the worker workspace. This research paper describes how workers can be benefited from having a personal net of crawlers (as Google does) collecting and organizing updated data relevant to their areas of interest and delivering these to their workspace.
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence Marina Santini
Query logs are an important source of information to surmize users intents'. Although Karlgren (2010) points out that “There are several reasons to be cautious in drawing too far-reaching conclusions: we cannot say for sure what the users were after; [...]“, some linguistic problems could be sorted out by applying more advanced text/content analytics, such as register/sublanguage identification and terminology classification (see Friberg Heppin, 2011) . In this presentation, I will argue that query logs can be considered a digital textual genre alike emails, blogs, chats, tweets and so forth. All these genres contain unstructured information that, still today, is difficult to leverage upon satisfactorily. The hypothesis that I would like to put forward in this workshop is that query logs might be easier to exploit to extract useful information and actionable intelligence than other digital genres.
This presentation was provided by Vinod Chachra of VTLS Inc. during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.
"Analysis of Different Text Classification Algorithms: An Assessment "ijtsrd
Theoretical Classification of information has become a significant research region. The way toward ordering archives into predefined classifications dependent on their substance is Text characterization. It is the mechanized task of common language writings to predefined classifications. The essential prerequisite of content recovery frameworks is content characterization, which recover messages because of a client inquiry, and content getting frameworks, which change message here and there, for example, responding to questions, creating outlines or removing information. In this paper we are concentrating the different grouping calculations. Order is the way toward isolating the information to certain gatherings that can demonstration either conditionally or freely. Our fundamental point is to show the examination of the different characterization calculations like K nn, Na¯ve Bayes, Decision Tree, Random Forest and Support Vector Machine SVM with quick digger and discover which calculation will be generally reasonable for the clients. Adarsh Raushan | Prof. Ankur Taneja | Prof. Naveen Jain "Analysis of Different Text Classification Algorithms: An Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29869.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/29869/analysis-of-different-text-classification-algorithms-an-assessment/adarsh-raushan
overview of python programming language.pptxdmsidharth
Python, born out of Guido van Rossum's vision in the late 1980s and formally introduced in 1991, stands tall as one of the foremost programming languages in today's digital landscape. Its journey from inception to dominance reflects a narrative of simplicity, versatility, and unwavering community support. At its core, Python embodies a design philosophy that prioritizes readability, fostering an environment where developers can express their ideas with clarity and conciseness. This philosophy, encapsulated in the famous maxim "Readability counts," has been instrumental in attracting a diverse array of practitioners, ranging from seasoned professionals to eager novices.
ExperTwin: An Alter Ego in Cyberspace for Knowledge WorkersCarlos Toxtli
ExperTwin is a Knowledge Advantage Machine (KAM) that is able to collect data from your areas of interest and present it in-time, in-context and in place to the worker workspace. This research paper describes how workers can be benefited from having a personal net of crawlers (as Google does) collecting and organizing updated data relevant to their areas of interest and delivering these to their workspace.
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence Marina Santini
Query logs are an important source of information to surmize users intents'. Although Karlgren (2010) points out that “There are several reasons to be cautious in drawing too far-reaching conclusions: we cannot say for sure what the users were after; [...]“, some linguistic problems could be sorted out by applying more advanced text/content analytics, such as register/sublanguage identification and terminology classification (see Friberg Heppin, 2011) . In this presentation, I will argue that query logs can be considered a digital textual genre alike emails, blogs, chats, tweets and so forth. All these genres contain unstructured information that, still today, is difficult to leverage upon satisfactorily. The hypothesis that I would like to put forward in this workshop is that query logs might be easier to exploit to extract useful information and actionable intelligence than other digital genres.
This presentation was provided by Vinod Chachra of VTLS Inc. during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.
"Analysis of Different Text Classification Algorithms: An Assessment "ijtsrd
Theoretical Classification of information has become a significant research region. The way toward ordering archives into predefined classifications dependent on their substance is Text characterization. It is the mechanized task of common language writings to predefined classifications. The essential prerequisite of content recovery frameworks is content characterization, which recover messages because of a client inquiry, and content getting frameworks, which change message here and there, for example, responding to questions, creating outlines or removing information. In this paper we are concentrating the different grouping calculations. Order is the way toward isolating the information to certain gatherings that can demonstration either conditionally or freely. Our fundamental point is to show the examination of the different characterization calculations like K nn, Na¯ve Bayes, Decision Tree, Random Forest and Support Vector Machine SVM with quick digger and discover which calculation will be generally reasonable for the clients. Adarsh Raushan | Prof. Ankur Taneja | Prof. Naveen Jain "Analysis of Different Text Classification Algorithms: An Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29869.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/29869/analysis-of-different-text-classification-algorithms-an-assessment/adarsh-raushan
overview of python programming language.pptxdmsidharth
Python, born out of Guido van Rossum's vision in the late 1980s and formally introduced in 1991, stands tall as one of the foremost programming languages in today's digital landscape. Its journey from inception to dominance reflects a narrative of simplicity, versatility, and unwavering community support. At its core, Python embodies a design philosophy that prioritizes readability, fostering an environment where developers can express their ideas with clarity and conciseness. This philosophy, encapsulated in the famous maxim "Readability counts," has been instrumental in attracting a diverse array of practitioners, ranging from seasoned professionals to eager novices.
3.Implementation with NOSQL databases Document Databases (Mongodb).pptxRushikeshChikane2
this Chapter gives information about Document Based Database and Graph based Database. It gives their basic structures, Features,applications ,Limitations and use cases
Python is a widely-used, high-level programming language known for its simplicity, readability, and extensive library support. It is favored by developers for its ease of use and ability to handle diverse tasks, making it suitable for various applications ranging from web development to data analysis and artificial intelligence.
Data-Oriented Programming in Java. Data-Oriented Programming (DOP) focuses on decreasing the complexity of the Object-Oriented Programming (OOP) application systems by rethinking data, i.e., separating data and code. DOP divides the system into two core components (Data entities and Code Module), where you can think about each separately.
Page 18Goal Implement a complete search engine. Milestones.docxsmile790243
Page 1/8
Goal: Implement a complete search engine. Milestones Overview
Milestone Goal #1 Produce an initial index for the corpus and a basic retrieval component
#2 Complete Search System
Page 2/8
PROJECT: SEARCH ENGINE Corpus: all ICS web pages We will provide you with the crawled data as a zip file (webpages_raw.zip). This contains the downloaded content of the ICS web pages that were crawled by a previous quarter. You are expected to build your search engine index off of this data. Main challenges: Full HTML parsing, File/DB handling, handling user input (either using command line or desktop GUI application or web interface) COMPONENT 1 - INDEX: Create an inverted index for all the corpus given to you. You can either use a database to store your index (MongoDB, Redis, memcached are some examples) or you can store the index in a file. You are free to choose an approach here. The index should store more than just a simple list of documents where the token occurs. At the very least, your index should store the TF-IDF of every term/document. Sample Index:
Note: This is a simplistic example provided for your understanding. Please do not consider this as the expected index format. A good inverted index will store more information than this. Index Structure: token – docId1, tf-idf1 ; docId2, tf-idf2
Example: informatics – doc_1, 5 ; doc_2, 10 ; doc_3, 7 You are encouraged to come up with heuristics that make sense and will help in retrieving relevant search results. For e.g. - words in bold and in heading (h1, h2, h3) could be treated as more important than the other words. These are useful metadata that could be added to your inverted index data. Optional (1 point for each meta data item up to 2 points max):: Extra credit will be given for ideas that improve the quality of the retrieval, so you may add more metadata to your index, if you think it will help improve the quality of the retrieval. For this, instead of storing a simple TF-IDF count for every page, you can store more information related to the page (e.g. position of the words in the page). To store this information, you need to design your index in such a way that it can store and retrieve all this metadata efficiently. Your index lookup during search should not be horribly slow, so pay attention to the structure of your index COMPONENT 2 – SEARCH AND RETRIEVE: Your program should prompt the user for a query. This doesn’t need to be a Web interface, it can be a console prompt. At the time of the query, your program will look up your index, perform some calculations (see ranking below) and give out the ranked list of pages that are relevant for the query.
COMPONENT 3 - RANKING:
At the very least, your ranking formula should include tf-idf scoring, but you should feel free to add additional components to this formula if you think they improve the retrieval. Optional (1 point for each parameter up to 2 points max): Extra credit will be given if your ranking formula includes par.
Modern Search: Using ML & NLP advances to enhance search and discoveryAll Things Open
Presented at Open Source Charlotte
Presented by Grant Ingersoll
Title: Modern Search: Using ML & NLP advances to enhance search and discovery
Abstract: With the recent advances in natural language processing and machine learning thanks to deep learning and large general purpose models, many search applications are confronted with how best to upgrade their systems, if at all. In this talk, we’ll look at practical ways to enhance search using neural and other machine learning techniques across ranking, content understanding and query understanding. We’ll also look at the tradeoffs of traditional approaches with a goal of helping you decide what’s best for your application.
For more info on Open Source Charlotte: https://www.meetup.com/open-source-charlotte/
TOPS Technologies offer Professional Java Training in Ahmedabad.
Ahmedabad Office (C G Road)
903 Samedh Complex,
Next to Associated Petrol Pump,
CG Road,
Ahmedabad 380009.
http://www.tops-int.com/live-project-training-java.html
Most experienced IT Training Institute in Ahmedabad known for providing Java course as per Industry Standards and Requirement.
Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model.
But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing.
We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define.
This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.
COAR Next Generation Repositories WG - Text mining and Recommender system sto...petrknoth
One of the key aims of the COAR NGR group is to help us to overcome the challenges that still make it difficult to move beyond repositories as document silos. The group wants to see a globally interoperable network of repositories and global services built on top of repositories fulfilling the expectations of users in the 21st century. During this talk, I will address two use cases the COAR NGR working group aims to enable: text and data mining and recommender systems.
Presented on Tuesday, August 7, at the 2018 LRCN (Librarians' Registration Council of Nigeria) National Workshop on Electronic Resource Management Systems in Libraries, held at the University of Nigeria, Nsukka, Enugu State, Nigeria
The world has changed and having one huge server won’t do the job anymore, when you’re talking about vast amounts of data, growing all the time the ability to Scale Out would be your savior. Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
This lecture will be about the basics of Apache Spark and distributed computing and the development tools needed to have a functional environment.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the seventh session of NISO's 2023 Training Series on Text and Data Mining. Session seven, "Vector Databases and Semantic Searching" was held on Thursday, November 30, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the closing segment of the NISO training series "AI & Prompt Design." Session Eight: Limitations and Potential Solutions, was held on May 23, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the sixth segment of the NISO training series "AI & Prompt Design." Session Six: Text Classification with LLMs, was held on May 9, 2024.
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3.Implementation with NOSQL databases Document Databases (Mongodb).pptxRushikeshChikane2
this Chapter gives information about Document Based Database and Graph based Database. It gives their basic structures, Features,applications ,Limitations and use cases
Python is a widely-used, high-level programming language known for its simplicity, readability, and extensive library support. It is favored by developers for its ease of use and ability to handle diverse tasks, making it suitable for various applications ranging from web development to data analysis and artificial intelligence.
Data-Oriented Programming in Java. Data-Oriented Programming (DOP) focuses on decreasing the complexity of the Object-Oriented Programming (OOP) application systems by rethinking data, i.e., separating data and code. DOP divides the system into two core components (Data entities and Code Module), where you can think about each separately.
Page 18Goal Implement a complete search engine. Milestones.docxsmile790243
Page 1/8
Goal: Implement a complete search engine. Milestones Overview
Milestone Goal #1 Produce an initial index for the corpus and a basic retrieval component
#2 Complete Search System
Page 2/8
PROJECT: SEARCH ENGINE Corpus: all ICS web pages We will provide you with the crawled data as a zip file (webpages_raw.zip). This contains the downloaded content of the ICS web pages that were crawled by a previous quarter. You are expected to build your search engine index off of this data. Main challenges: Full HTML parsing, File/DB handling, handling user input (either using command line or desktop GUI application or web interface) COMPONENT 1 - INDEX: Create an inverted index for all the corpus given to you. You can either use a database to store your index (MongoDB, Redis, memcached are some examples) or you can store the index in a file. You are free to choose an approach here. The index should store more than just a simple list of documents where the token occurs. At the very least, your index should store the TF-IDF of every term/document. Sample Index:
Note: This is a simplistic example provided for your understanding. Please do not consider this as the expected index format. A good inverted index will store more information than this. Index Structure: token – docId1, tf-idf1 ; docId2, tf-idf2
Example: informatics – doc_1, 5 ; doc_2, 10 ; doc_3, 7 You are encouraged to come up with heuristics that make sense and will help in retrieving relevant search results. For e.g. - words in bold and in heading (h1, h2, h3) could be treated as more important than the other words. These are useful metadata that could be added to your inverted index data. Optional (1 point for each meta data item up to 2 points max):: Extra credit will be given for ideas that improve the quality of the retrieval, so you may add more metadata to your index, if you think it will help improve the quality of the retrieval. For this, instead of storing a simple TF-IDF count for every page, you can store more information related to the page (e.g. position of the words in the page). To store this information, you need to design your index in such a way that it can store and retrieve all this metadata efficiently. Your index lookup during search should not be horribly slow, so pay attention to the structure of your index COMPONENT 2 – SEARCH AND RETRIEVE: Your program should prompt the user for a query. This doesn’t need to be a Web interface, it can be a console prompt. At the time of the query, your program will look up your index, perform some calculations (see ranking below) and give out the ranked list of pages that are relevant for the query.
COMPONENT 3 - RANKING:
At the very least, your ranking formula should include tf-idf scoring, but you should feel free to add additional components to this formula if you think they improve the retrieval. Optional (1 point for each parameter up to 2 points max): Extra credit will be given if your ranking formula includes par.
Modern Search: Using ML & NLP advances to enhance search and discoveryAll Things Open
Presented at Open Source Charlotte
Presented by Grant Ingersoll
Title: Modern Search: Using ML & NLP advances to enhance search and discovery
Abstract: With the recent advances in natural language processing and machine learning thanks to deep learning and large general purpose models, many search applications are confronted with how best to upgrade their systems, if at all. In this talk, we’ll look at practical ways to enhance search using neural and other machine learning techniques across ranking, content understanding and query understanding. We’ll also look at the tradeoffs of traditional approaches with a goal of helping you decide what’s best for your application.
For more info on Open Source Charlotte: https://www.meetup.com/open-source-charlotte/
TOPS Technologies offer Professional Java Training in Ahmedabad.
Ahmedabad Office (C G Road)
903 Samedh Complex,
Next to Associated Petrol Pump,
CG Road,
Ahmedabad 380009.
http://www.tops-int.com/live-project-training-java.html
Most experienced IT Training Institute in Ahmedabad known for providing Java course as per Industry Standards and Requirement.
Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model.
But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing.
We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define.
This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.
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One of the key aims of the COAR NGR group is to help us to overcome the challenges that still make it difficult to move beyond repositories as document silos. The group wants to see a globally interoperable network of repositories and global services built on top of repositories fulfilling the expectations of users in the 21st century. During this talk, I will address two use cases the COAR NGR working group aims to enable: text and data mining and recommender systems.
Presented on Tuesday, August 7, at the 2018 LRCN (Librarians' Registration Council of Nigeria) National Workshop on Electronic Resource Management Systems in Libraries, held at the University of Nigeria, Nsukka, Enugu State, Nigeria
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This presentation was provided by William Mattingly of the Smithsonian Institution, during the third segment of the NISO training series "AI & Prompt Design." Session Three: Beginning Conversations, was held on April 18, 2024.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. 1. GPT-4o
2. Multimodal LLMs
3. Vector Databases and Semantic Search
4. What is Text Classification?
5. How is it useful?
6. Traditional Approaches
7. LLMs and Text Classification
8. Open Source LLMs
Goals
4. GPT-4o
A New Model
● Pricing: GPT-4o is 50% cheaper than
GPT-4 Turbo, coming in at $5/M input
and $15/M output tokens).
● Rate limits: GPT-4o’s rate limits are 5x
higher than GPT-4 Turbo—up to 10
million tokens per minute.
● Speed: GPT-4o is 2x as fast as GPT-4
Turbo.
● Vision: GPT-4o’s vision capabilities
perform better than GPT-4 Turbo in
evals related to vision capabilities.
● Multilingual: GPT-4o has improved
support for non-English languages over
GPT-4 Turbo.
● GPT-4o currently has a context window
of 128k and has a knowledge cut-off
date of October 2023.
5. GPT-4o
A New Model
● Released This week
● Purely Multimodal
● Exceptionally fast (low latency)
● Cheaper
● Available via the API and Chat
7. GPT-4o
Multimodal
Text, Audio, and Video are all
vectorized by the same model and
treated the same way. In other
words, a text that describes a
beach would be very similar in
vector space to an image of a
beach.
12. Vector
Database
How do we use a vector
database?
● We populate a vector database
with by using a machine
learning model to vectorize
data and send them to the
database.
14. Vector
Database
Why use a vector database?
● Vector databases allow users
to store vector data in a way
that allows users to query it
and find similarity based on a
vector-level similarity, rather
than explicit human-defined
similarity.
15. Vector
Database
What is it?
● A vector database holds
numerous vectors or
embeddings of data.
Sometimes, the database will
also store the original data
alongside these vectors.
18. Vector Database
Stacks
What is available to us?
● Python, Annoy, Streamlit
○ Cheap, easy to deploy, great for
smaller datasets, but requires a
little bit of knowledge to build from
scratch
○ Best for smaller databases (under
10,000 data)
● Python, txtAI
○ Cheap and easy to use, more
resource intensive but easy to
deploy
○ Allows for easy interpretability (via
highlighting)
22. Text
Classification
Emails
"Congratulations! You've won a
$1,000 Walmart gift card. Click here
to claim your prize."
"Limited time offer: Buy one get one
free on all items in our store."
"Dear customer, your account has
been temporarily suspended.
Please update your information to
restore access."
23. Text
Classification
Sentiment
"I love this product! It works exactly
as described."
"The product arrived late and was
damaged. Very disappointed."
"It's okay, not great but not terrible
either."
"Excellent service and quick
delivery. Highly recommend!"
27. Text
Classification
Multilabel Classification
Assigns multiple (or single) labels to
a single text instance, where each
label represents a different
category.
News categorization where an
article can belong to multiple
categories such as "politics,"
"economy," and "health."
28. Text
Classification
Hierarchical Classification
Classifies text into a hierarchy of
categories, where categories are
structured in a tree-like hierarchy.
Document classification in a library
where documents are classified into
categories like "science," "arts,"
"technology," with subcategories
under each (e.g., "science" can
have "physics," "chemistry,"
"biology").
30. Open Source
ML
Overview
Open source machine learning, like
open source software (OSS), is
driven by the public. It has several
components: open source datasets,
open source machine learning
models, and open source
applications.
The best resource: HuggingFace
31. Open Source
ML
Datasets
● Datasets for training task-
specific models
○ NER
○ Text Classification
○ Image Classification
○ Object Detection
● Datasets for training language
models
○ Unannotated collections of texts
● Dataset Cards
○ Task
○ Language
○ Biases
32. Open Source
ML
Models
● Trained Machine Learning
Models for specific Tasks
○ NER
○ Text Classification
○ Image Classification
○ Object Detection
○ ASR
○ HTR
○ OCR
● Trained machine learning
language models (including
LLMs)
● Dataset Cards
○ Task
○ Language
○ Biases
33. Open Source
ML
Benefits and Limitations
● Benefits
○ Open, meaning they are freely
available to use (though
sometimes with commercial
limitations)
○ Publicly Critiqued
○ Understanding of the Data
● Limitations
○ Closed models are better in many
cases (BUT!!! That gap is closing).