While language models in generative AI focus on textual data, vision language models (VLMs) bridge the gap between textual and visual data. Before we explore Moondream 2, let’s understand VLMs better.
Understanding vision language models
VLMs combine computer vision (CV) and natural language processing (NLP), enabling them to understand and connect visual information with textual data.
Some key capabilities of VLMs include image captioning, visual question answering, and image retrieval. It learns these tasks by training on datasets that pair images with their corresponding textual description. There are several large vision language models available in the market including GPT-4v, LLaVA, and BLIP-2.
However, these are large vision models requiring heavy computational resources to produce effective results, and that too at slow inference speeds. The solution has been presented in the form of small VLMs that provide a balance between efficiency and performance.
In this blog, we will look deeper into Moondream 2, a small vision language model.
What is Moondream 2?
Moondream 2 is an open-source vision language model. With only 1.86 billion parameters, it is a tiny VLM with weights from SigLIP and Phi-1.5. It is designed to operate seamlessly on devices with limited computational resources.
Weights for Moondream 2
Let’s take a closer look at the defined weights for Moondream2.
SigLIP (Sigmoid Loss for Language Image Pre-Training)
It is a newer and simpler method that helps the computer learn just by looking at pictures and their captions, one at a time, making it faster and more effective, especially when training with lots of data. It is similar to a CLIP (Contrastive Language–Image Pre-training) model.
However, Moondream 2 has replaced softmax loss in CLIP with a simple pairwise sigmoid loss. The change ensures better performance because sigmoid loss only focuses on image-text pairs. Without the need for a global view of all pairwise data within a batch, the process becomes faster and more efficient.
It is a small language model with 1.3 billion parameters and transformer-based architecture. Developed by Microsoft, the model was trained on 30 billion words, including information from its predecessor, Phi-1 and 20 billion words artificially created by another AI model, GPT-3.5.
With its unique training method, Phi-1.5 has mastered different skills required of a language model, like understanding common sense, following complex instructions, comprehending language, and retaining information. Moreover, it even outperforms its larger counterparts.
A closer look into Moondream 2
It is the latest and updated version of the model previously known as Moondream 1. Below is a quick comparison of the two models, highlighting the basic parameters of improvement.
Comparing Moondream 1 and 2
Like typical vision language models, Moondream 2 interprets the visual data to provide textual responses based on the information. Some demos included in its repository highlight its capabilities. Let’s look at these demos below:
Demo 1
Moondream 2 was provided with the following image:
Based on this image, the VLM was asked some relevant questions.
Question: What is the girl doing?
Answer: The girl is sitting at a table and eating a large hamburger.
Question: What color is the girl’s hair?
Answer: The girl’s hair is white.
The interaction with Moondream 2 highlights that the VLM is capable of understanding the basic image properties like the girl’s hair color. Moreover, it can also interpret the actions of the actors in an image from their position in a specific instance.
Demo 2
The VLM was provided with another image.
The demo focused on asking the following:
Question: What is this?
Answer: The image features a computer server rack, which is a large metal structure designed to hold and organize multiple computer components, such as motherboards, cooling systems, and other peripherals. The rack is filled with various computer parts, including multiple computer chips, wires, and other electronic components. The rack is placed on a carpeted floor, and there is a couch in the background, suggesting that the setup is likely in a living or working space.
Question: What is behind the stand?
Answer: There is a brick wall behind the stand.
The above demo highlights the ability of Moondream 2 to explore and interpret complex visual outputs in great detail. The VLM provides in-depth textual information from the visual data. It also presents spacial understanding of the image components.
Hence, Moondream 2 is a promising addition to the world of vision language models with its refined capabilities to interpret visual data and provide in-depth textual output. Since we understand the strengths of the VLM, it is time to explore its drawbacks or weaknesses.
Here’s a list of 7 books you must explore when learning about computer vision
Limitations of Moondream 2
Before you explore the world of Moondream 2, you must understand its limitations when dealing with visual and textual data.
Generating inaccurate statements
It is important to understand that Moondream 2 may generate inaccurate statements, especially for complex topics or situations requiring real-world understanding. The model might also struggle to grasp subtle details or hidden meanings within instructions.
Presenting unconscious bias
Like any other VLM, Moondream 2 is also a product of the data is it trained on. Thus, it can reflect the biases of the world, perpetuating stereotypes or discriminatory views.
As a user, it’s crucial to be aware of this potential bias and to approach the model’s outputs with a critical eye. Don’t blindly accept everything it generates; use your own judgment and fact-check when necessary.
Mirroring prompts
VLMs will reflect the prompts provided to them. Hence, if a user prompts the model to generate offensive or inappropriate content, the model may comply. It’s important to be mindful of the prompts and avoid asking the model to create anything harmful or hurtful.
In conclusion…
To sum it up, Moondream 2 is a promising step in the development of vision language models. Powered by its key components and compact size, the model is efficient and fast. However, like any language model we use nowadays, Moondream 2 also requires its users to be responsible for ensuring the creation of useful content.
If you are ready to experiment with Moondream 2 now, install the necessary files and start right away! Here’s a look at what the VLM’s user interface looks like.
Knowledge graphs and LLMs are the building blocks of the most recent advancements happening in the world of artificial intelligence (AI). Combining knowledge graphs (KGs) and LLMs produces a system that has access to a vast network of factual information and can understand complex language.
The system has the potential to use this accessibility to answer questions, generate textual outputs, and engage with other NLP tasks. This blog aims to explore the potential of integrating knowledge graphs and LLMs, navigating through the promise of revolutionizing AI.
Introducing knowledge graphs and LLMs
Before we understand the impact and methods of integrating KGs and LLMs, let’s visit the definition of the two concepts.
What are knowledge graphs (KGs)?
They are a visual web of information that focuses on connecting factual data in a meaningful manner. Each set of data is represented as a node with edges building connections between them. This representational storage of data allows a computer to recognize information and relationships between the data points.
KGs organize data to highlight connections and new relationships in a dataset. Moreover, it enabled improved search results as knowledge graphs integrate the contextual information to provide more relevant results.
What are large language models (LLMs)?
LLMs are a powerful tool within the world of AI using deep learning techniques for general-purpose language generation and other natural language processing (NLP) tasks. They train on massive amounts of textual data to produce human-quality texts.
Large language models have revolutionized human-computer interactions with the potential for further advancements. However, LLMs are limited in the factual grounding of their results. It makes LLMs able to produce high-quality and grammatically accurate results that can be factually inaccurate.
An overview of knowledge graphs and LLMs – Source: arXiv
Combining KGs and LLMs
Within the world of AI and NLP, integrating the concepts of KGs and LLMs has the potential to open up new avenues of exploration. While knowledge graphs cannot understand language, they are good at storing factual data. Unlike KGs, LLMs excel in language understanding but lack factual grounding.
Combining the two entities brings forward a solution that addresses the weaknesses of both. The strengths of KGs and LLMs cover each concept’s limitations, producing more accurate and better-represented results.
Frameworks to combine KGs and LLMs
It is one thing to talk about combining knowledge graphs and large language models, implementing the idea requires planning and research. So far, researchers have explored three different frameworks aiming to integrate KGs and LLMs for enhanced outputs.
Frameworks for integrating KGs and LLMs – Source: arXiv
KG-enhanced LLMs
This framework focuses on using knowledge graphs for training LLMs. The factual knowledge and relationship links in the KGs become accessible to the LLMs in addition to the traditional textual data during the training phase. A LLM can then learn from the information available in KGs.
As a result, LLMs can get a boost in factual accuracy and grounding by incorporating the data from KGs. It will also enable the models to fact-check the outputs and produce more accurate and informative results.
LLM-augmented KGs
This design shifts the structure of the first framework. Instead of KGs enhancing LLMs, they leverage the reasoning power of large language models to improve knowledge graphs. It makes LLMs smart assistants to improve the output of KGs, curating their information representation.
Moreover, this framework can leverage LLMs to find problems and inconsistencies in information connections of KGs. The high reasoning of LLMs also enables them to infer new relationships in a knowledge graph, enriching its outputs.
This builds a pathway to create more comprehensive and reliable knowledge graphs, benefiting from the reasoning and inference abilities of LLMs.
This framework proposes a mutually beneficial relationship between the two AI components. Each entity works to improve the other through a feedback loop. It is designed in the form of a continuous learning cycle between LLMs and KGs.
It can be viewed as a concept that combines the two above-mentioned frameworks into a single design where knowledge graphs enhance language model outputs and LLMs analyze and improve KGs.
It results in a dynamic cycle where KGs and LLMs constantly improve each other. The iterative design of this integration framework leads to a more powerful and intelligent system overall.
While we have looked at the three different frameworks of integration of KGs and LLMs, the synergized LLMs + KGs is the most advanced approach in this field. It promises to unlock the full potential of both entities, supporting the creation of superior AI systems with enhanced reasoning, knowledge representation, and text generation capabilities.
Future of LLM and KG integration
Combining the powers of knowledge graphs and large language models holds immense potential in various fields. Some plausible possibilities are discussed below.
Educational revolution
With access to knowledge graphs, LLMs can generate personalized educational content for students, encompassing a wide range of subjects and topics. The data can be used to generate interactive lessons, provide detailed feedback, and answer questions with factual accuracy.
Enhancing scientific research
The integrated frameworks provide an ability to analyze vast amounts of scientific data, identify patterns, and even suggest new hypotheses. The combination has the potential to accelerate scientific research across various fields.
Intelligent customer service
With useful knowledge representations of KGs, LLMs can generate personalized and more accurate support. It will also enhance their ability to troubleshoot issues and offer improved recommendations, providing an intelligent customer experience to the users of any enterprise.
Thus, the integration of knowledge graphs and LLMs has the potential to boost the development of AI-powered tasks and transform the field of NLP.
The recently unveiled Falcon Large Language Model, boasting 180 billion parameters, has surpassed Meta’s LLaMA 2, which had 70 billion parameters.
Falcon 180B: A game-changing open-source language model
The artificial intelligence community has a new champion in Falcon 180B, an open-source large language model (LLM) boasting a staggering 180 billion parameters, trained on a colossal dataset. This powerhouse newcomer has outperformed previous open-source LLMs on various fronts.
Falcon AI, particularly Falcon LLM 40B, represents a significant achievement by the UAE’s Technology Innovation Institute (TII). The “40B” designation indicates that this Large Language Model boasts an impressive 40 billion parameters.
Notably, TII has also developed a 7 billion parameter model, trained on a staggering 1500 billion tokens. In contrast, the Falcon LLM 40B model is trained on a dataset containing 1 trillion tokens from RefinedWeb. What sets this LLM apart is its transparency and open-source nature.
Falcon operates as an autoregressive decoder-only model and underwent extensive training on the AWS Cloud, spanning two months and employing 384 GPUs. The pretraining data predominantly comprises publicly available data, with some contributions from research papers and social media conversations.
Significance of Falcon AI
The performance of Large Language Models is intrinsically linked to the data they are trained on, making data quality crucial. Falcon’s training data was meticulously crafted, featuring extracts from high-quality websites, sourced from the RefinedWeb Dataset. This data underwent rigorous filtering and de-duplication processes, supplemented by readily accessible data sources. Falcon’s architecture is optimized for inference, enabling it to outshine state-of-the-art models such as those from Google, Anthropic, Deepmind, and LLaMa, as evidenced by its ranking on the OpenLLM Leaderboard.
Beyond its impressive capabilities, Falcon AI distinguishes itself by being open-source, allowing for unrestricted commercial use. Users have the flexibility to fine-tune Falcon with their data, creating bespoke applications harnessing the power of this Large Language Model. Falcon also offers Instruct versions, including Falcon-7B-Instruct and Falcon-40B-Instruct, pre-trained on conversational data. These versions facilitate the development of chat applications with ease.
Hugging Face Hub Release
Announced through a blog post by the Hugging Face AI community, Falcon 180B is now available on Hugging Face Hub.
This latest-model architecture builds upon the earlier Falcon series of open-source LLMs, incorporating innovations like multiquery attention to scale up to its massive 180 billion parameters, trained on a mind-boggling 3.5 trillion tokens.
Unprecedented Training Effort
Falcon 180B represents a remarkable achievement in the world of open-source models, featuring the longest single-epoch pretraining to date. This milestone was reached using 4,096 GPUs working simultaneously for approximately 7 million GPU hours, with Amazon SageMaker facilitating the training and refinement process.
Surpassing LLaMA 2 & commercial models
To put Falcon 180B’s size in perspective, its parameters are 2.5 times larger than Meta’s LLaMA 2 model, previously considered one of the most capable open-source LLMs. Falcon 180B not only surpasses LLaMA 2 but also outperforms other models in terms of scale and benchmark performance across a spectrum of natural language processing (NLP) tasks.
It achieves a remarkable 68.74 points on the open-access model leaderboard and comes close to matching commercial models like Google’s PaLM-2, particularly on evaluations like the HellaSwag benchmark.
Falcon AI: A strong benchmark performance
Falcon 180B consistently matches or surpasses PaLM-2 Medium on widely used benchmarks, including HellaSwag, LAMBADA, WebQuestions, Winogrande, and more. Its performance is especially noteworthy as an open-source model, competing admirably with solutions developed by industry giants.
Comparison with ChatGPT
Compared to ChatGPT, Falcon 180B offers superior capabilities compared to the free version but slightly lags behind the paid “plus” service. It typically falls between GPT 3.5 and GPT-4 in evaluation benchmarks, making it an exciting addition to the AI landscape.
Falcon AI with LangChain
LangChain is a Python library designed to facilitate the creation of applications utilizing Large Language Models (LLMs). It offers a specialized pipeline known as HuggingFacePipeline, tailored for models hosted on HuggingFace. This means that integrating Falcon with LangChain is not only feasible but also practical.
Installing LangChain package
Begin by installing the LangChain package using the following command:
This command will fetch and install the latest LangChain package, making it accessible for your use.
Creating a pipeline for Falcon model
Next, let’s create a pipeline for the Falcon model. You can do this by importing the required components and configuring the model parameters:
Here, we’ve utilized the HuggingFacePipeline object, specifying the desired pipeline and model parameters. The ‘temperature’ parameter is set to 0, reducing the model’s inclination to generate imaginative or off-topic responses. The resulting object, named ‘llm,’ stores our Large Language Model configuration.
PromptTemplate and LLMChain
LangChain offers tools like PromptTemplate and LLMChain to enhance the responses generated by the Large Language Model. Let’s integrate these components into our code:
In this section, we define a template for the PromptTemplate, outlining how our LLM should respond, emphasizing humor in this case. The template includes a question placeholder labeled {query}. This template is then passed to the PromptTemplate method and stored in the ‘prompt’ variable.
To finalize our setup, we combine the Large Language Model and the Prompt using the LLMChain method, creating an integrated model configured to generate humorous responses.
Putting it into action
Now that our model is configured, we can use it to provide humorous answers to user questions. Here’s an example code snippet:
In this example, we presented the query “How to reach the moon?” to the model, which generated a humorous response. The Falcon-7B-Instruct model followed the prompt’s instructions and produced an appropriate and amusing answer to the query.
This demonstrates just one of the many possibilities that this new open-source model, Falcon AI, can offer.
A promising future
Falcon 180B’s release marks a significant leap forward in the advancement of large language models. Beyond its immense parameter count, it showcases advanced natural language capabilities from the outset.
With its availability on Hugging Face, the model is poised to receive further enhancements and contributions from the community, promising a bright future for open-source AI.
Large language models (LLMs) are AI models that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. They are trained on massive amounts of text data, and they can learn to understand the nuances of human language.
In this blog, we will take a deep dive into LLMs, including their building blocks, such as embeddings, transformers, and attention. We will also discuss the different applications of LLMs, such as machine translation, question answering, and creative writing.
To test your knowledge, we have included a crossword or quiz at the end of the blog. So, what are you waiting for? Let’s crack the code of large language models!
LLMs are typically built using a transformer architecture. Transformers are a type of neural network that are well-suited for natural language processing tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language.
They are typically trained on clusters of computers or even on cloud computing platforms. The training process can take weeks or even months, depending on the size of the dataset and the complexity of the model.
20 essential terms for crafting LLM-powered applications
1. Large language model (LLM)
Large language models (LLMs) are AI models that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. The building blocks of an LLM are embeddings, transformers, attention, and loss functions. Embeddings are vectors that represent the meaning of words or phrases. Transformers are a type of neural network that are well-suited for NLP tasks. Attention is a mechanism that allows the LLM to focus on specific parts of the input text. The loss function is used to measure the error between the LLM’s output and the desired output. The LLM is trained to minimize the loss function.
2. OpenAI
OpenAI is a non-profit research company that develops and deploys artificial general intelligence (AGI) in a safe and beneficial way. AGI is a type of artificial intelligence that can understand and reason like a human being. OpenAI has developed a number of LLMs, including GPT-3, Jurassic-1 Jumbo, and DALL-E 2.
GPT-3 is a large language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Jurassic-1 Jumbo is a larger language model that is still under development. It is designed to be more powerful and versatile than GPT-3. DALL-E 2 is a generative AI model that can create realistic images from text descriptions.
3. Generative AI
Generative AI is a type of AI that can create new content, such as text, images, or music. LLMs are a type of generative AI. They are trained on large datasets of text and code, which allows them to learn the patterns of human language. This allows them to generate text that is both coherent and grammatically correct.
Generative AI has a wide range of potential applications. It can be used to create new forms of art and entertainment, to develop new educational tools, and to improve the efficiency of businesses. It is still a relatively new field, but it is rapidly evolving.
4. ChatGPT
ChatGPT is a large language model (LLM) developed by OpenAI. It is designed to be used in chatbots. ChatGPT is trained on a massive dataset of text and code, which allows it to learn the patterns of human conversation. This allows it to hold conversations that are both natural and engaging. ChatGPT is also capable of answering questions, providing summaries of factual topics, and generating different creative text formats.
5. Bard
Bard is a large language model (LLM) developed by Google AI. It is still under development, but it has been shown to be capable of generating text, translating languages, and writing different kinds of creative content. Bard is trained on a massive dataset of text and code, which allows it to learn the patterns of human language. This allows it to generate text that is both coherent and grammatically correct. Bard is also capable of answering your questions in an informative way, even if they are open ended, challenging, or strange.
6. Foundation models
Foundation models are a family of large language models (LLMs) developed by Google AI. They are designed to be used as a starting point for developing other AI models. Foundation models are trained on massive datasets of text and code, which allows them to learn the patterns of human language. This allows them to be used to develop a wide range of AI applications, such as chatbots, machine translation, and question-answering systems.
7. LangChain
LangChain is a text-to-image diffusion model that can be used to generate images from text descriptions. It is based on the Transformer model and is trained on a massive dataset of text and images. LangChain is still under development, but it has the potential to be a powerful tool for creative expression and problem-solving.
8. Llama Index
Llama Index is a data framework for large language models (LLMs). It provides tools to ingest, structure, and access private or domain-specific data. LlamaIndex can be used to connect LLMs to a variety of data sources, including APIs, PDFs, documents, and SQL databases. It also provides tools to index and query data, so that LLMs can easily access the information they need.
Llama Index is a relatively new project, but it has already been used to build a number of interesting applications. For example, it has been used to create a chatbot that can answer questions about the stock market, and a system that can generate creative text formats, like poems, code, scripts, musical pieces, email, and letters.
9. Redis
Redis is an in-memory data store that can be used to store and retrieve data quickly. It is often used as a cache for web applications, but it can also be used for other purposes, such as storing embeddings. Redis is a popular choice for NLP applications because it is fast and scalable.
10. Streamlit
Streamlit is a framework for creating interactive web apps. It is easy to use and does not require any knowledge of web development. Streamlit is a popular choice for NLP applications because it allows you to quickly and easily build web apps that can be used to visualize and explore data.
11. Cohere
Cohere is a large language model (LLM) developed by Google AI. It is known for its ability to generate human-quality text. Cohere is trained on a massive dataset of text and code, which allows it to learn the patterns of human language. This allows it to generate text that is both coherent and grammatically correct. Cohere is also capable of translating languages, writing different kinds of creative content, and answering your questions in an informative way.
12. Hugging Face
Hugging Face is a company that develops tools and resources for NLP. It offers a number of popular open-source libraries, including Transformer models and datasets. Hugging Face also hosts a number of online communities where NLP practitioners can collaborate and share ideas.
LLM Crossword
13. Midjourney
Midjourney is a LLM developed by Midjourney. It is a text-to-image AI platform that uses a large language model (LLM) to generate images from natural language descriptions. The user provides a prompt to Midjourney, and the platform generates an image that matches the prompt. Midjourney is still under development, but it has the potential to be a powerful tool for creative expression and problem-solving.
14. Prompt Engineering
Prompt engineering is the process of crafting prompts that are used to generate text with LLMs. The prompt is a piece of text that provides the LLM with information about what kind of text to generate.
Prompt engineering is important because it can help to improve the performance of LLMs. By providing the LLM with a well-crafted prompt, you can help the model to generate more accurate and creative text. Prompt engineering can also be used to control the output of the LLM. For example, you can use prompt engineering to generate text that is similar to a particular style of writing, or to generate text that is relevant to a particular topic.
When crafting prompts for LLMs, it is important to be specific, use keywords, provide examples, and be patient. Being specific helps the LLM to generate the desired output, but being too specific can limit creativity.
Using keywords helps the LLM focus on the right topic, and providing examples helps the LLM learn what you are looking for. It may take some trial and error to find the right prompt, so don’t give up if you don’t get the desired output the first time.
Embeddings are a type of vector representation of words or phrases. They are used to represent the meaning of words in a way that can be understood by computers. LLMs use embeddings to learn the relationships between words. Embeddings are important because they can help LLMs to better understand the meaning of words and phrases, which can lead to more accurate and creative text generation. Embeddings can also be used to improve the performance of other NLP tasks, such as natural language understanding and machine translation.
Fine-tuning is the process of adjusting the parameters of a large language model (LLM) to improve its performance on a specific task. Fine-tuning is typically done by feeding the LLM a dataset of text that is relevant to the task.
For example, if you want to fine-tune an LLM to generate text about cats, you would feed the LLM a dataset of text that contains information about cats. The LLM will then learn to generate text that is more relevant to the task of generating text about cats.
Fine-tuning can be a very effective way to improve the performance of an LLM on a specific task. However, it can also be a time-consuming and computationally expensive process.
17. Vector databases
Vector databases are a type of database that is optimized for storing and querying vector data. Vector data is data that is represented as a vector of numbers. For example, an embedding is a vector that represents the meaning of a word or phrase.
Vector databases are often used to store embeddings because they can efficiently store and retrieve large amounts of vector data. This makes them well-suited for tasks such as natural language processing (NLP), where embeddings are often used to represent words and phrases.
Vector databases can be used to improve the performance of fine-tuning by providing a way to store and retrieve large datasets of text that are relevant to the task. This can help to speed up the fine-tuning process and improve the accuracy of the results.
18. Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP tasks include text analysis, machine translation, and question answering. LLMs are a powerful tool for NLP. NLP is a complex field that covers a wide range of tasks. Some of the most common NLP tasks include:
Text analysis: This involves extracting information from text, such as the sentiment of a piece of text or the entities that are mentioned in the text.
For example, an NLP model could be used to determine whether a piece of text is positive or negative, or to identify the people, places, and things that are mentioned in the text.
Machine translation: This involves translating text from one language to another.
For example, an NLP model could be used to translate a news article from English to Spanish.
Question answering: This involves answering questions about text.
For example, an NLP model could be used to answer questions about the plot of a movie or the meaning of a word.
Speech recognition: This involves converting speech into text.
For example, an NLP model could be used to transcribe a voicemail message.
Text generation: This involves generating text, such as news articles or poems.
For example, an NLP model could be used to generate a creative poem or a news article about a current event.
19. Tokenization
Tokenization is the process of breaking down a piece of text into smaller units, such as words or subwords. Tokenization is a necessary step before LLMs can be used to process text. When text is tokenized, each word or subword is assigned a unique identifier. This allows the LLM to track the relationships between words and phrases.
There are many different ways to tokenize text. The most common way is to use word boundaries. This means that each word is a token. However, some LLMs can also handle subwords, which are smaller units of text that can be combined to form words.
For example, the word “cat” could be tokenized as two subwords: “c” and “at”. This would allow the LLM to better understand the relationships between words, such as the fact that “cat” is related to “dog” and “mouse”.
20. Transformer models
Transformer models are a type of neural network that are well-suited for NLP tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language. Transformer models work by first creating a representation of each word in the text. This representation is then used to calculate the relationship between each word and the other words in the text.
The Transformer model is a powerful tool for NLP because it can learn the complex relationships between words and phrases. This allows it to perform NLP tasks with a high degree of accuracy. For example, a Transformer model could be used to translate a sentence from English to Spanish while preserving the meaning of the sentence.
Embeddings are a key building block of large language models. For the unversed, large language models (LLMs) are composed of several key building blocks that enable them to efficiently process and understand natural language data.
A large language model (LLM) is a type of artificial intelligence model that is trained on a massive dataset of text. This dataset can be anything from books and articles to websites and social media posts. The LLM learns the statistical relationships between words, phrases, and sentences in the dataset, which allows it to generate text that is similar to the text it was trained on.
How is a large language model built?
LLMs are typically built using a transformer architecture. Transformers are a type of neural network that are well-suited for natural language processing tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language.
LLMs are so large that they cannot be run on a single computer. They are typically trained on clusters of computers or even on cloud computing platforms. The training process can take weeks or even months, depending on the size of the dataset and the complexity of the model.
Key building blocks of large language model
Foundation of LLM
1. Embeddings
Embeddings are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space. They allow the model to convert discrete tokens into a format that can be processed by the neural network. LLMs learn embeddings during training to capture relationships between words, like synonyms or analogies.
2. Tokenization
Tokenization is the process of converting a sequence of text into individual words, subwords, or tokens that the model can understand. LLMs use subword algorithms like BPE or wordpiece to split text into smaller units that capture common and uncommon words. This approach helps to limit the model’s vocabulary size while maintaining its ability to represent any text sequence.
3. Attention
Attention mechanisms in LLMs, particularly the self-attention mechanism used in transformers, allow the model to weigh the importance of different words or phrases. By assigning different weights to the tokens in the input sequence, the model can focus on the most relevant information while ignoring less important details. This ability to selectively focus on specific parts of the input is crucial for capturing long-range dependencies and understanding the nuances of natural language.
4. Pre-training
Pre-training is the process of training an LLM on a large dataset, usually unsupervised or self-supervised, before fine-tuning it for a specific task. During pretraining, the model learns general language patterns, relationships between words, and other foundational knowledge.
The process creates a pretrained model that can be fine-tuned using a smaller dataset for specific tasks. This reduces the need for labeled data and training time while achieving good results in natural language processing tasks (NLP).
5. Transfer learning
Transfer learning is the technique of leveraging the knowledge gained during pretraining and applying it to a new, related task. In the context of LLMs, transfer learning involves fine-tuning a pretrained model on a smaller, task-specific dataset to achieve high performance on that task. The benefit of transfer learning is that it allows the model to benefit from the vast amount of general language knowledge learned during pretraining, reducing the need for large labeled datasets and extensive training for each new task.
Understanding embeddings
Embeddings are used to represent words as vectors of numbers, which can then be used by machine learning models to understand the meaning of text. Embeddings have evolved over time from the simplest one-hot encoding approach to more recent semantic embedding approaches.
Embeddings – By Data Science Dojo
Types of embeddings
Type of embedding
Description
Use-cases
Word embeddings
Represent individual words as vectors of numbers.
Text classification, text summarization, question answering, machine translation
Sentence embeddings
Represent entire sentences as vectors of numbers.
Text classification, text summarization, question answering, machine translation
Bag-of-words (BoW) embeddings
Represent text as a bag of words, where each word is assigned a unique ID.
Text classification, text summarization
TF-IDF embeddings
Represent text as a bag of words, where each word is assigned a weight based on its frequency and inverse document frequency.
Text classification, text summarization
GloVe embeddings
Learn word embeddings from a corpus of text by using global co-occurrence statistics.
Text classification, text summarization, question answering, machine translation
Word2Vec embeddings
Learn word embeddings from a corpus of text by predicting the surrounding words in a sentence.
Text classification, text summarization, question answering, machine translation
Classic approaches to embeddings
In the early days of natural language processing (NLP), embeddings were simply one-hot encoded. Zero vector represents each word with a single one at the index that matches its position in the vocabulary.
1. One-hot encoding
One-hot encoding is the simplest approach to embedding words. It represents each word as a vector of zeros, with a single one at the index corresponding to the word’s position in the vocabulary. For example, if we have a vocabulary of 10,000 words, then the word “cat” would be represented as a vector of 10,000 zeros, with a single one at index 0.
One-hot encoding is a simple and efficient way to represent words as vectors of numbers. However, it does not take into account the context in which words are used. This can be a limitation for tasks such as text classification and sentiment analysis, where the context of a word can be important for determining its meaning.
For example, the word “cat” can have multiple meanings, such as “a small furry mammal” or “to hit someone with a closed fist.” In one-hot encoding, these two meanings would be represented by the same vector. This can make it difficult for machine learning models to learn the correct meaning of words.
2. TF-IDF
TF-IDF (term frequency-inverse document frequency) is a statistical measure that is used to quantify the importanceThe process creates a pretrained model that can be fine-tuned using a smaller dataset for specific tasks. This reduces the need for labeled data and training time while achieving good results in natural language processing tasks (NLP). of a word in a document. It is a widely used technique in natural language processing (NLP) for tasks such as text classification, information retrieval, and machine translation.
TF-IDF is calculated by multiplying the term frequency (TF) of a word in a document by its inverse document frequency (IDF). TF measures the number of times a word appears in a document, while IDF measures how rare a word is in a corpus of documents.
The TF-IDF score for a word is high when the word appears frequently in a document and when the word is rare in the corpus. This means that TF-IDF scores can be used to identify words that are important in a document, even if they do not appear very often.
Understanding TF-IDF with example
Here is an example of how TF-IDF can be used to create word embeddings. Let’s say we have a corpus of documents about cats. We can calculate the TF-IDF scores for all of the words in the corpus. The words with the highest TF-IDF scores will be the words that are most important in the corpus, such as “cat,” “dog,” “fur,” and “meow.”
We can then create a vector for each word, where each element of the vector represents the TF-IDF score for that word. The TF-IDF vector for the word “cat” would be high, while the TF-IDF vector for the word “dog” would also be high, but not as high as the TF-IDF vector for the word “cat.”
The TF-IDF word embeddings can then be used by a machine-learning model to classify documents about cats. The model would first create a vector representation of a new document. Then, it would compare the vector representation of the new document to the TF-IDF word embeddings. The document would be classified as a “cat” document if its vector representation is most similar to the TF-IDF word embeddings for “cat.”
Count-based and TF-IDF
To address the limitations of one-hot encoding, count-based and TF-IDF techniques were developed. These techniques take into account the frequency of words in a document or corpus.
Count-based techniques simply count the number of times each word appears in a document. TF-IDF techniques take into account both the frequency of a word and its inverse document frequency.
Count-based and TF-IDF techniques are more effective than one-hot encoding at capturing the context in which words are used. However, they still do not capture the semantic meaning of words.
Capturing local context with N-grams
To capture the semantic meaning of words, n-grams can be used. N-grams are sequences of n-words. For example, a 2-gram is a sequence of two words.
N-grams can be used to create a vector representation of a word. The vector representation is based on the frequencies of the n-grams that contain the word.
N-grams are a more effective way to capture the semantic meaning of words than count-based or TF-IDF techniques. However, they still have some limitations. For example, they are not able to capture long-distance dependencies between words.
Semantic encoding techniques
Semantic encoding techniques are the most recent approach to embedding words. These techniques use neural networks to learn vector representations of words that capture their semantic meaning.
One of the most popular semantic encoding techniques is Word2Vec. Word2Vec uses a neural network to predict the surrounding words in a sentence. The network learns to associate words that are semantically similar with similar vector representations.
Semantic encoding techniques are the most effective way to capture the semantic meaning of words. They are able to capture long-distance dependencies between words and they are able to learn the meaning of words even if they have never been seen before. Here are some other semantic encoding techniques:
1. ELMo: Embeddings from language models
ELMo is a type of word embedding that incorporates both word-level characteristics and contextual semantics. It is created by taking the outputs of all layers of a deep bidirectional language model (bi-LSTM) and combining them in a weighted fashion. This allows ELMo to capture the meaning of a word in its context, as well as its own inherent properties.
The intuition behind ELMo is that the higher layers of the bi-LSTM capture context, while the lower layers capture syntax. This is supported by empirical results, which show that ELMo outperforms other word embeddings on tasks such as POS tagging and word sense disambiguation.
ELMo is trained to predict the next word in a sequence of words, a task called language modeling. This means that it has a good understanding of the relationships between words. When assigning an embedding to a word, ELMo takes into account the words that surround it in the sentence. This allows it to generate different embeddings for the same word depending on its context.
Understanding ELMo with example
For example, the word “play” can have multiple meanings, such as “to perform” or “a game.” In standard word embeddings, each instance of the word “play” would have the same representation. However, ELMo can distinguish between these different meanings by taking into account the context in which the word appears. In the sentence “The Broadway play premiered yesterday,” for example, ELMo would assign the word “play” an embedding that reflects its meaning as a theater production.
ELMo has been shown to be effective for a variety of natural language processing tasks, including sentiment analysis, question answering, and machine translation. It is a powerful tool that can be used to improve the performance of NLP models.
2. GloVe
GloVe is a statistical method for learning word embeddings from a corpus of text. GloVe is similar to Word2Vec, but it uses a different approach to learning the vector representations of words.
How GloVe works
GloVe works by creating a co-occurrence matrix. The co-occurrence matrix is a table that shows how often two words appear together in a corpus of text. For example, the co-occurrence matrix for the words “cat” and “dog” would show how often the words “cat” and “dog” appear together in a corpus of text.
GloVe then uses a machine learning algorithm to learn the vector representations of words from the co-occurrence matrix. The machine learning algorithm learns to associate words that appear together frequently with similar vector representations.
3. Word2Vec
Word2Vec is a semantic encoding technique that is used to learn vector representations of words. Word vectors represent word meaning and can enhance machine learning models for tasks like text classification, sentiment analysis, and machine translation.
Word2Vec works by training a neural network on a corpus of text. The neural network is trained to predict the surrounding words in a sentence. The network learns to associate words that are semantically similar with similar vector representations.
There are two main variants of Word2Vec:
Continuous Bag-of-Words (CBOW): The CBOW model predicts the surrounding words in a sentence based on the current word. For example, the model might be trained to predict the words “the” and “dog” given the word “cat”.
Skip-gram: The skip-gram model predicts the current word based on the surrounding words in a sentence. For example, the model might be trained to predict the word “cat” given the words “the” and “dog”.
Word2Vec has been shown to be effective for a variety of tasks, including:
Text classification: Word2Vec can be used to train a classifier to classify text into different categories, such as news articles, product reviews, and social media posts.
Sentiment analysis: Word2Vec can be used to train a classifier to determine the sentiment of text, such as whether it is positive, negative, or neutral.
Machine translation: Word2Vec can be used to train a machine translation model to translate text from one language to another.
GloVe
Word2Vec
ELMo
Accuracy
More accurate
Less accurate
More accurate
Training time
Faster to train
Slower to train
Slower to train
Scalability
More scalable
Less scalable
Less scalable
Ability to capture long-distance dependencies
Not as good at capturing long-distance dependencies
Better at capturing long-distance dependencies
Best at capturing long-distance dependencies
Word2Vec vs Dense word embeddings
Word2Vec is a neural network model that learns to represent words as vectors of numbers. Word2Vec is trained on a large corpus of text, and it learns to predict the surrounding words in a sentence.
Word2Vec can be used to create dense word embeddings. Dense word embeddings are vectors that have a fixed size, regardless of the size of the vocabulary. This makes them easy to use with machine learning models.
Dense word embeddings have been shown to be effective in a variety of NLP tasks, such as text classification, sentiment analysis, and machine translation.
Semantic encoding techniques are the most recent approach to embedding words and are the most effective way to capture their semantic meaning. They are able to capture long-distance dependencies between words and they are able to learn the meaning of words even if they have never been seen before.
Safe to say, embeddings are a powerful tool that can be used to improve the performance of machine learning models for a variety of tasks, such as text classification, sentiment analysis, and machine translation. As research in NLP continues to evolve, we can expect to see even more sophisticated embeddings that can capture even more of the nuances of human language.
The buzz surrounding large language models is wreaking havoc and for all the good reason! The game-changing technological marvels have got everyone talking and have to be topping the charts in 2023.
What are large language models?
A large language model (LLM) is a machine learning model capable of performing various natural language processing (NLP) tasks, including text generation, text classification, question answering in conversational settings, and language translation. The term “large” in this context refers to the model’s extensive set of parameters, which are the values it can autonomously adjust during the learning process. Some highly successful LLMs possess hundreds of billions of these parameters.
LLMs undergo training with vast amounts of data and utilize self-supervised learning to predict the next token in a sentence based on its context. They can be used to perform a variety of tasks, including:
Natural language understanding: LLMs can understand the meaning of text and code, and can answer questions about it.
Natural language generation: LLMs can generate text that is similar to human-written text.
Translation: LLMs can translate text from one language to another.
Summarization: LLMs can summarize text into a shorter, more concise version.
Question answering: LLMs can answer questions about text.
Code generation: LLMs can generate code, such as Python or Java code.
Understanding Large Language Models
Best examples of large language models
Let’s explore a range of noteworthy large language models that have made waves in the field:
1. BERT (Bidirectional Encoder Representations from Transformers)
BERT is a revolutionary transformer-based model that underwent extensive pre-training on vast amounts of text data. Its prowess lies in natural language processing (NLP) tasks like sentiment analysis, question-answering, and text classification.
2. GPT-3 (Generative Pretrained Transformer 3)
OpenAI’s flagship creation, GPT-3, stands tall as one of the most advanced AI models worldwide. Trained on massive text datasets, it boasts an exceptional ability to generate human-like responses across diverse topics, retaining an extensive conversational memory.
3. XLM-R (Cross-lingual Language Model – RoBERTa)
Facebook AI Research’s transformer-based behemoth, XLM-R, takes multilingual capabilities to new heights. It undergoes pre-training on colossal multilingual text corpora and excels in NLP tasks such as text classification, machine translation, and question-answering.
4. Whisper
OpenAI’s Whisper enters the scene as a powerful automatic speech recognition (ASR) system. Its training on a staggering 680,000 hours of diverse and multilingual data empowers it to transcribe speech in multiple languages and perform English translations with improved accuracy, even amidst accents, background noise, and technical jargon.
5. T5 (Text-to-Text Transfer Transformer)
Developed by Google Research, T5 proves its mettle as a versatile large language model. It tackles various NLP tasks like text generation, summarization, and translation through the magic of transfer learning, adapting its capabilities to different contexts.
A marvel in multilingual translation, M2M-100 obliterates language barriers. With training encompassing an astonishing 2,200 language directions, this model achieves remarkable translation accuracy across 100 languages without relying on English-centric data.
7. MPNet (Masked and Permuted Language Modeling Pre-training Network):
MPNet introduces a novel approach to language model pre-training. By combining masked language modeling (MLM) and permuted language modeling (PLM), it takes token dependency into account, building upon BERT’s classification methodologies.
As we assess these models’ performance and capabilities, it’s crucial to acknowledge their specificity for particular NLP tasks. The choice of the optimal model depends on the task at hand. Large language models exhibit impressive proficiency across various NLP domains and hold immense potential for transforming customer engagement, operational efficiency, and beyond.
What are some of the benefits of LLMs?
LLMs have a number of benefits over traditional AI methods. They are able to understand the meaning of text and code in a much more sophisticated way. This allows them to perform tasks that would be difficult or impossible for traditional AI methods.LLMs are also able to generate text that is very similar to human-written text. This makes them ideal for applications such as chatbots and translation tools.
Applications for large language models
1. Streamlining language generation in IT:
Discover how generative AI can elevate IT teams by optimizing processes and delivering innovative solutions. Witness its potential in:
Recommending and creating knowledge articles and forms
Updating and editing knowledge repositories
Real-time translation of knowledge articles, forms, and employee communications
Crafting product documentation effortlessly
2. Boosting efficiency with language summarization
Explore how generative AI can revolutionize IT support teams, automating tasks and expediting solutions. Experience its benefits in:
Extracting topics, symptoms, and sentiments from IT tickets
Clustering IT tickets based on relevant topics
Generating narratives from analytics
Summarizing IT ticket solutions and lengthy threads
Condensing phone support transcripts and highlighting critical solutions
3. Unleashing code and data generation potential
Witness the transformative power of generative AI in IT infrastructure and chatbot development, saving time by automating laborious tasks such as:
Suggesting conversation flows and follow-up patterns
Generating training data for conversational AI systems
Testing knowledge articles and forms for relevance
Assisting in code generation for repetitive snippets from online sources
Future possibilities of LLMs
The future possibilities of LLMs are very exciting. They have the potential to revolutionize the way we interact with computers. They could be used to create new types of applications, such as chatbots that can understand and respond to natural language, or translation tools that can translate text with near-human accuracy.
LLMs could also be used to improve our understanding of the world. They could be used to analyze large datasets of text and code and to identify patterns and trends that would be difficult or impossible to identify with traditional methods.
Wrapping up
LLMs represent a highly potent and promising technology that presents numerous possibilities for various applications. While still in the development phase, these models have the capacity to fundamentally transform our interactions with computers.
Data Science Dojo specializes in delivering a diverse array of services aimed at enabling organizations to harness the capabilities of Large Language Models. Leveraging our extensive expertise and experience, we provide customized solutions that perfectly align with your specific needs and goals.
Learn how the synergy of AI and Machine Learning algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression.
Artificial intelligence or AI as it is commonly called is a vast field of study that deals with empowering computers to be “Intelligent”. This intelligence can manifest in different ways, but typically, it results in the automation of mundane tasks. However, the advancements in AI have led to automation in more sophisticated tasks as well.
One of the most common applications of AI in a sophisticated task is text processing and manipulation. Which is also our topic today. Specifically, the paraphrasing of text with the help of AI. The most revolutionary technology that enables this is called machine learning.
Machine learning algorithms
Machine learning is a subset of AI. So, when you say AI, it automatically includes machine learning as well. Now, we will take a look at how machine learning works in Paraphrasing tools.
Role of machine learning algorithms in paraphrasing tools
Machine learning by itself is also a vast field. There are a lot of ways in which a computer can process and manipulate text with machine learning algorithms.
You must have heard the name GPT if you are interested in text processing. GPT is one of the most popular machine-learning models used for text processing. It belongs to a class of models called “Transformers” which are classified among deep learning models.
And that was just one model. Transformers are the most popular when it comes to text processing and programmers have a lot of options to choose from. Many paraphrase generators nowadays utilize transformers in their back end for changing the given text.
Most paraphrasing tools that are powered by AI are developed using Python because Python has a lot of prebuilt libraries for NLP (natural language processing).
NLP is yet another application of machine learning algorithms. It allows computer systems to parse and understand text much in the same way a human would. So, let’s take a look at how a paraphrase generator works with these NLP libraries.We will check out a few different libraries and as such different transformers that are used nowadays for paraphrasing text.
1. Pegasus Transformer
This is a part of the Transformers library available in Python 3. You can download Pegasus using pip with simple instructions. Machine learning algorithms will transform our lives, from autonomous vehicles to personalized medicine.
Pegasus was originally created for summarizing, however, the good thing about machine learning is that models can be tuned to do different things. So even though Pegasus is for summarizing, it can still be used for paraphrasing.
Here’s how it works for paraphrasing.
The transformer is trained on a large database of text, such a database is called a “corpus”. This corpus contains sentence pairs and each pair includes an original sentence and its paraphrased version.By training on such a corpus, the transformer learns how different sentences mean the same thing. Then it can create new paraphrases of any given sentence, even the ones it did not train on.
2. T5 Transformer
T5 or text-to-text transfer transformer is a neural network architecture that can do a lot of things:
Summarizing
Translating
Question and answering
And of course, paraphrasing
A paraphrasing tool that uses the T5 transformer can give a variety of different results because it is trained on a massive amount of data. According to Google (the creators of T5), the T5 transformer was trained on Wikipedia, books, articles, and plenty of online web pages.
T5 uses unsupervised learning which means it’s not told what is what, and it is allowed to draw its own conclusions. While that gives it extreme flexibility, it also gives more room for making errors. That’s why always proofread any text you get from a paraphrasing tool as it could have mistakes.
3. Parrot Library
This particular library is not a transformer, but it uses similar techniques. It uses the same type of sequence-to-sequence architecture that is used in the T5 transformer.
Another similarity between the two is that Parrot is also trained on a corpus of sentence pairs where one sentence is original and the other is paraphrased. This allows it to find patterns and realize that different syntax can still have the same meaning.
Parrot uses a mix of supervised and unsupervised learning techniques. However, what sets Parrot apart from other models of paraphrasing is that it has two steps.
Step one creates a bunch of paraphrases for the given text. However, it does not finalize them right away.
Step 2 ranks the generated paraphrases and only selects the most highly ranked output. It uses a variety of factors to calculate rank and it is widely touted as one of the most accurate and fluent paraphrasing models available.
Conclusion
So, now you know something about how machine learning algorithms work in paraphrasing tools. These models are running on the server side of these tools, so the end user cannot see what is happening.
The tool forwards the input to the models, and they generate an output which is shown to the user. And that is the simplest description of paraphrasing with machine learning.
This blog discusses the different tasks and techniques used in natural language processing. We will be using python code to demo what and how each task works. We will also discuss why these tasks and techniques are essential for natural language processing.
Introduction
According to a survey, only 32 percent of the business data is put to work, and 68 percent goes unleveraged. Most data are often unstructured. According to estimations, 80 to 90 percent of business data is unstructured, and so are emails, reports, social media posts, websites, and documents. Using NLP techniques, it became possible for machines to manage and analyze unstructured data accurately and quickly.
Computers can now understand, manipulate, and interpret human language. Businesses use NLP to improve customer experience, listen to customer feedback, and find market gaps. Almost 50% of companies today use NLP applications, and 25% plan to do so in 12 months.
The future of customer care is NLP. Customers prefer mobile messaging and chatbots over the legacy voice channel. It is four times more accurate. According to the IBM market survey, 52% of global IT professionals reported using or planning to use NLP to improve customer experience. Chatbots can resolve 80% of routine tasks and customer questions with a 90% success rate by 2022. Estimates show that using NLP in chatbots will save companies USD 8 billion annually.
The NLP market was at 3 billion US dollars in 2017 and is predicted to rise to 43 billion US dollars in 2025, around 14 times higher.
Natural Language Processing (NLP)
Natural language processing is a branch of artificial intelligence that enables computers to analyze, understand, and drive meaning from a human language using machine learning and respond to it. NLP combines computational linguistics with artificial intelligence and machine learning to create an intelligent system capable of understanding and responding to text or voice data the same way humans do.
NLP analyzes the syntax and semantics of the text to understand the meaning and structure of human language. Then it transforms this linguistic knowledge into a machine-learning algorithm to solve real-world problems and perform specific tasks.
Natural language is challenging to comprehend, which makes NLP a challenging task. Mastering a language is easy for humans, but implementing NLP becomes difficult for machines because of the ambiguity and imprecision of natural language.
NLP requires syntactic and semantic analysis to convert human language into a machine-readable form that can be processed and interpreted.
Syntactic analysis
Syntactic analysis is the process of analyzing language with its formal grammatical rules. It is also known as syntax analysis or parsing formal grammatical rules applied to a group of words but not a single word. After verifying the correct syntax, it takes text data as input and creates a structural input representation. It creates a parse tree. A syntactically correct sentence does not necessarily make sense. It needs to be semantically correct to make sense.
Semantic analysis
Semantic analysis is the process of figuring out the meaning of the text. It enables computers to interpret the words by analyzing sentence structure and the relationship between individual words of the sentence. Because of language’s ambiguous and polysemic nature, semantic analysis is a particularly challenging area of NLP. It analyzes the sentence structure, word interaction, and other aspects to discover the meaning and topic of the text.
NLP tasks and techniques:
Before proceeding further, ensure you run the below code block to install all the dependencies.
Here are some everyday tasks performed in syntactic and semantic analysis:
Tokenization
Tokenization is a common task in NLP. It separates natural language text into smaller units called tokens. For example, in Sentence tokenization paragraph separates into sentences, and word tokenization splits the words of a sentence.
The code below shows an example of word tokenization using spaCy.
Code:
import spacynlp = spacy.load("en_core_web_sm")doc = nlp("Data Science Dojo is the leading platform providing data science training.")for token in doc: print(token.text)
Part of speech or grammatical tagging labels each word as an appropriate part of speech based on its definition and context. POS tagging helps create a parse tree that helps understand word relationships. It also helps in Named Entity Recognition, as most named entities are nouns, making it easier to identify them.
In the code below, we use pos_ attribute of the token to get the part of speech for the universal pos tag set.
Code:
import spacyfrom prettytable import PrettyTabletable = PrettyTable(['Token', 'Part of speech', 'Tag'])nlp = spacy.load("en_core_web_sm")doc = nlp("Data Science Dojo is the leading platform providing data science training.")for token in doc: table.add_row([token.text, token.pos_, token.tag_])print(table)
Dependency parsing is how grammatical structure in a sentence is analyzed to find out the related word and their relationship. Each relationship has one head and one dependent. Then, a label based on the nature of dependency is assigned between the head and the dependent.
Consistency parsing is a process by which phrase structure grammar is identified to visualize the entire syntactic structure.
In the code below, we created a dependency tree using the displacy visualizer of spacy.
Code:
import spacynlp = spacy.load("en_core_web_sm")doc = nlp("Data Science Dojo is the leading platform providing data science training.") spacy.displacy.render(doc, style="dep")
We use inflected forms of the word when we speak or write. These inflected forms are created by adding prefixes or suffixes to the root form. In the process of lemmatization and stemming, we are grouping similar inflected forms of a word into a single root word. In this way, we link all the words with the same meaning as a single word, which is simpler to analyze by the computer.
The word’s root form in lemmatization is lemma, and in stemming is a stem. Lemmatization and stemming do the same task of grouping inflected forms, but they are different. Lemmatization considers the word and its context in the sentence, while stemming only considers the single word. So, we consider POS tags in lemmatization but not in stemming. That is why lemma is an actual dictionary word, but stem might not be.
Now we are applying lemmatization using spacy.
Code:
import spacynlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])doc = nlp("Data Science Dojo is the leading platform providing data science training.")lemmatized = [token.lemma_ for token in doc]print("Original: \n", doc)print("\nAfter Lemmatization: \n", " ".join(lemmatized))
Output:
Original: Data Science Dojo is the leading platform providing data science training.After Lemmatization: Data Science Dojo is the lead platform to provide datum science training.
Unfortunately, spacy does not contain any function for stemming.
Let us use Porter Stemmer from nltk to see how stemming works.
Code:
import nltknltk.download('punkt')from nltk.stem import PorterStemmerfrom nltk.tokenize import word_tokenize ps = PorterStemmer()sentence = "Data Science Dojo is the leading platform providing data science training."words = word_tokenize(sentence)stemmed = [ps.stem(token) for token in words] print("Original: \n", " ".join(words))print("\nAfter Stemming: \n", " ".join(stemmed))
Output:
Original: Data Science Dojo is the leading platform providing data science training .After Stemming: data scienc dojo is the lead platform provid data scienc train .
Stop word removal
Stop words are the frequent words that are used in any natural language. However, they are not particularly useful for text analysis and NLP tasks. Therefore, we remove them, as they do not play any role in defining the meaning of the text.
Code:
import spacynlp = spacy.load("en_core_web_sm")doc = nlp("Data Science Dojo is the leading platform providing data science training.")token_list = [ token.text for token in doc ]filtered_sentence = [ word for word in token_list if nlp.vocab[word].is_stop == False ] print("Tokens:\n",token_list)print("\nAfter stop word removal:\n", filtered_sentence)
Named entity recognition is an NLP technique that extracts named entities from the text and categorizes them into semantic types like organization, people, quantity, percentage, location, time, etc. Identifying named entities helps identify the critical element in the text, which can help sort the unstructured data and find valuable information.
Code:
import spacyfrom prettytable import PrettyTablenlp = spacy.load("en_core_web_sm")doc = nlp("Data Science Dojo was founded in 2013 but it was a free Meetup group long before the official launch. With the aim to bring the knowledge of data science to everyone, we started hosting short Bootcamps with the most comprehensive curriculum. In 2019, the University of New Mexico (UNM) added our Data Science Bootcamp to their continuing education department. Since then, we've launched various other trainings such as Python for Data Science, Data Science for Managers and Business Leaders. So far, we have provided our services to more than 10,000 individuals and over 2000 organizations.")table = PrettyTable(["Entity", "Start Position", "End Position", "Label"])for ent in doc.ents: table.add_row([ent.text, ent.start_char, ent.end_char, ent.label_])print(table)spacy.displacy.render(doc, style="ent")
Sentiment analysis, also referred to as opinion mining, uses natural language processing to find and extract sentiments from the text. It determines whether the data is positive, negative, or neutral.
Some of the real-world applications of sentiment analysis are:
We have discussed natural language processing and what common tasks it performs in natural language processing. Then, we saw how we can perform different functions in spacy and nltk and why they are essential in natural language processing.
We know about the different tasks and techniques we perform in natural language processing, but we have yet to discuss the applications of natural language processing. For that, you can follow this blog.
This blog will discuss the different Natural Language Processing applications. We will see the applications and what problems they solve in our daily life.
Introduction
One of the essential things in the life of a human being is communication. We need to communicate with other human beings to deliver information, express our emotions, present ideas, and much more. The key to communication is language. We need a common language to communicate, which both ends of the conversation can understand. Doing this is possible for humans, but it might seem a bit difficult if we talk about communicating with a computer system or the computer system communicating with us.
But we have a solution for that, Artificial Intelligence, or more specifically, a branch of Artificial Intelligence known as Natural Language Processing (NLP). Natural Language Processing enables the computer system to understand and comprehend information the same way humans do. It helps the computer system understand the literal meaning and recognize the sentiments, tone, opinions, thoughts, and other components that construct a proper conversation.
Applications of Natural Language Processing
After making the computer understand human language, a question arises in our minds, how can we utilize this ability of a computer to benefit humankind?
Natural Language Processing Applications:
Let’s answer this question by going over some Natural Language Processing applications and understanding how they decrease our workload and help us complete many time-taking tasks more quickly and efficiently.
1. Email filtering
Email is a part of our everyday life. Whether it is related to work or studies or many other things, we find ourselves plunged into the pile of emails. We receive all kinds of emails from various sources; some are work-related or from our dream school or university, while others are spam or promotional emails. Here Natural Language Processing comes to work. It identifies and filters incoming emails into “important” or “spam” and places them into their respective designations.
2. Language translation
There are as many languages in this world as there are cultures, but not everyone understands all these languages. As our world is now a global village owing to the dawn of technology, we need to communicate with other people who speak a language that might be foreign to us. Natural Language processing helps us by translating the language with all its sentiments.
3. Smart assistants
In today’s world, every new day brings in a new smart device, making this world smarter and smarter by the day. And this advancement is not just limited to machines. We have advanced enough technology to have smart assistants, such as Siri, Alexa, and Cortana. We can talk to them like we talk to normal human beings, and they even respond to us in the same way.
All of this is possible because of Natural Language Processing. It helps the computer system understand our language by breaking it into parts of speech, root stem, and other linguistic features. It not only helps them understand the language but also in processing its meaning and sentiments and answering back in the same way humans do.
4. Document analysis
Another one of NLP’s applications is document analysis. Companies, colleges, schools, and other such places are always filled to the brim with data, which needs to be sorted out properly, maintained, and searched for. All this could be done using NLP. It not only searches a keyword but also categorizes it according to the instructions and saves us from the long and hectic work of searching for a single person’s information from a pile of files. It is not only limited to this but also helps its user to inform decision-making on claims and risk management.
5. Online searches
In this world full of challenges and puzzles, we must constantly find our way by getting the required information from available sources. One of the most extensive information sources is the internet. We type what we want to search and checkmate! We have got what we wanted. But have you ever thought about how you get these results even when you do not know the exact keywords you need to search for the needed information? Well, the answer is obvious.
It is again Natural Language Processing. It helps search engines understand what is asked of them by comprehending the literal meaning of words and the intent behind writing that word, hence giving us the results, we want.
6. Predictive text
A similar application to online searches is predictive text. It is something we use whenever we type anything on our smartphones. Whenever we type a few letters on the screen, the keyboard gives us suggestions about what that word might be and when we have written a few words, it starts suggesting what the next word could be. These predictive texts might be a little off in the beginning.
Still, as time passes, it gets trained according to our texts and starts to suggest the next word correctly even when we have not written a single letter of the next word. All this is done using NLP by making our smartphones intelligent enough to suggest words and learn from our texting habits.
7. Automatic summarization
With the increasing inventions and innovations, data has also increased. This increase in data has also expanded the scope of data processing. Still, manual data processing is time taking and is prone to error. NLP has a solution for that, too, it can not only summarize the meaning of information, but it can also understand the emotional meaning hidden in the information. Thus, making the summarization process quick and impeccable.
8. Sentiment analysis
The daily conversations, the posted content and comments, book, restaurant, and product reviews, hence almost all the conversations and texts are full of emotions. Understanding these emotions is as important as understanding the word-to-word meaning. We as humans can interpret emotional sentiments in writings and conversations, but with the help of natural language processing, computer systems can also understand the sentiments of a text along with its literal meaning.
9. Chatbots
With the increase in technology, everything has been digitalized, from studying to shopping, booking tickets, and customer service. Instead of waiting a long time to get some short and instant answers, the chatbot replies instantly and accurately. NLP gives these chatbots conversational capabilities, which help them respond appropriately to the customer’s needs instead of just bare-bones replies.
Chatbots also help in places where human power is less or is not available round the clock. Chatbots operating on NLP also have emotional intelligence, which helps them understand the customer’s emotional sentiments and respond to them effectively.
10. Social media monitoring
Nowadays, every other person has a social media account where they share their thoughts, likes, dislikes, experiences, etc., which tells a lot about the individuals. We do not only find information about individuals but also about the products and services. The relevant companies can process this data to get information about their products and services to improve or amend them. NLP comes into play here. It enables the computer system to understand unstructured social media data, analyze it and produce the required results in a valuable form for companies.
Conclusion:
We now understand that NLP has many applications, spreading its wings in almost every field. Help decrease manual labor and do the tasks accurately and efficiently.
Natural Language Processing is a key Data Science skill. Learn how to expand your knowledge with R programming books on Text Analytics.
It is my firm conviction that Natural Language Processing/Text Analytics is a must-have skill for any practicing Data Scientist.
From analyzing customer feedback in NSAT surveys to scraping Microsoft’s internal job postings for analyzing popular technical skills, to segmenting customers via textual features, I have universally found that Text Analytics is a wildly useful skill.
R programming books – Sources to learn from
Not surprisingly, I am often asked by students of our Data Science Bootcamp, folks that I mentor on Data Science and my LinkedIn contacts about the subject of Text Analytics. The good news is that there are many great resources for the R programmer to learn Text Analytics.
What follows is a practical curriculum where the only required knowledge is basic R programming skills. I have read all of the books referenced below and can attest that studying the curriculum will have you mastering Text Analytics in no time!
Book cover of Text Analytics with R for Students of Literature by Matthew L. Jockers
is quite simply the best, most straightforward introduction to working with text that I have found. Professor Jockers illustrates many of the fundamentals using out of the box R programming. This book provides a great foundation for anyone looking to get started in Text Analytics with R.
Book cover of Taming Text by Grant, Thomas, and Andrew
is the next stop on the Text Analytics journey. While this book is primarily written for Java programmers, there is a lot of theory that is immensely useful for R programmers learning to work with text. Additionally, the book covers the OpenNLP Java library which is available to R programmers via the excellent openNLP package.
R programming logo
The CRAN NLP Task View illustrates the wide-ranging Text Analytics support for the R programmer. Unfortunately, it also illustrates that the landscape is fractured as well. However, a couple of packages are worthy of study. The tm package is often the go-to Text Analytics package for R programmers. However, the new quanteda package shows a lot of promise. Lastly, the excellent openNLP package deserves a second callout.
Book cover of Introduction to Information Retrieval for Text Analytics by Christopher, Prabhakar, and Hinrich
while focused primarily on the problem of search, nevertheless, contains a wealth of theory and understanding (e.g., the Vector Space Model) to take the R programmer to the next level. The text is language agnostic, is quite excellent, and free!
While the Natural Language Toolkit (NLTK) is Python-based, the book on the subject of NLP is a wealth of goodness to the R programmer. I put this resource last in the list as learning the above conceptual material and R packages provides the necessary background to translate some of the concepts (e.g., chunking) into the R context. Awesome stuff, and free to boot!
There you have it, a practical curriculum for the R programmer to ramp into Text Analytics. Don’t hesitate to reach out if you have any questions or comments – I monitor my blog almost continually.
