Analysis of the evolution of advanced transformer-based language models: Expe...IAESIJAI
Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
EMPLOYING PIVOT LANGUAGE TECHNIQUE THROUGH STATISTICAL AND NEURAL MACHINE TRA...ijnlc
The quality of Neural Machine Translation (NMT) systems like Statistical Machine Translation (SMT) systems, heavily depends on the size of training data set, while for some pairs of languages, high-quality parallel data are poor resources. In order to respond to this low-resourced training data bottleneck reality, we employ the pivoting approach in both neural MT and statistical MT frameworks. During our experiments on the Persian-Spanish, taken as an under-resourced translation task, we discovered that, the aforementioned method, in both frameworks, significantly improves the translation quality in comparison to the standard direct translation approach.
Our project is about guessing the correct missing
word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
modeling, while the other is neural language models.
Statistical language modeling depend on the frequency of the
relation between words and here we use Markov chain. Since
neural language models uses artificial neural networks which
uses deep learning, here we use BERT which is the state of art
in language modeling provided by google.
Ara--CANINE: Character-Based Pre-Trained Language Model for Arabic Language U...IJCI JOURNAL
Recent advancements in the field of natural language processing have markedly enhanced the capability of machines to comprehend human language. However, as language models progress, they require continuous architectural enhancements and different approaches to text processing. One significant challenge stems from the rich diversity of languages, each characterized by its distinctive grammar resulting ina decreased accuracy of language models for specific languages, especially for low-resource languages. This limitation is exacerbated by the reliance of existing NLP models on rigid tokenization methods, rendering them susceptible to issues with previously unseen or infrequent words. Additionally, models based on word and subword tokenization are vulnerable to minor typographical errors, whether they occur naturally or result from adversarial misspellings. To address these challenges, this paper presents the utilization of a recently proposed free-tokenization method, such as Cannine, to enhance the comprehension of natural language. Specifically, we employ this method to develop an Arabic-free tokenization language model. In this research, we will precisely evaluate our model’s performance across a range of eight tasks using Arabic Language Understanding Evaluation (ALUE) benchmark. Furthermore, we will conduct a comparative analysis, pitting our free-tokenization model against existing Arabic language models that rely on sub-word tokenization. By making our pre-training and fine-tuning models accessible to the Arabic NLP community, we aim to facilitate the replication of our experiments and contribute to the advancement of Arabic language processing capabilities. To further support reproducibility and open-source collaboration, the complete source code and model checkpoints will be made publicly available on our Huggingface1 . In conclusion, the results of our study will demonstrate that the free-tokenization approach exhibits comparable performance to established Arabic language models that utilize sub-word tokenization techniques. Notably, in certain tasks, our model surpasses the performance of some of these existing models. This evidence underscores the efficacy of free-tokenization in processing the Arabic language, particularly in specific linguistic contexts.
Analysis of the evolution of advanced transformer-based language models: Expe...IAESIJAI
Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
EMPLOYING PIVOT LANGUAGE TECHNIQUE THROUGH STATISTICAL AND NEURAL MACHINE TRA...ijnlc
The quality of Neural Machine Translation (NMT) systems like Statistical Machine Translation (SMT) systems, heavily depends on the size of training data set, while for some pairs of languages, high-quality parallel data are poor resources. In order to respond to this low-resourced training data bottleneck reality, we employ the pivoting approach in both neural MT and statistical MT frameworks. During our experiments on the Persian-Spanish, taken as an under-resourced translation task, we discovered that, the aforementioned method, in both frameworks, significantly improves the translation quality in comparison to the standard direct translation approach.
Our project is about guessing the correct missing
word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
modeling, while the other is neural language models.
Statistical language modeling depend on the frequency of the
relation between words and here we use Markov chain. Since
neural language models uses artificial neural networks which
uses deep learning, here we use BERT which is the state of art
in language modeling provided by google.
Ara--CANINE: Character-Based Pre-Trained Language Model for Arabic Language U...IJCI JOURNAL
Recent advancements in the field of natural language processing have markedly enhanced the capability of machines to comprehend human language. However, as language models progress, they require continuous architectural enhancements and different approaches to text processing. One significant challenge stems from the rich diversity of languages, each characterized by its distinctive grammar resulting ina decreased accuracy of language models for specific languages, especially for low-resource languages. This limitation is exacerbated by the reliance of existing NLP models on rigid tokenization methods, rendering them susceptible to issues with previously unseen or infrequent words. Additionally, models based on word and subword tokenization are vulnerable to minor typographical errors, whether they occur naturally or result from adversarial misspellings. To address these challenges, this paper presents the utilization of a recently proposed free-tokenization method, such as Cannine, to enhance the comprehension of natural language. Specifically, we employ this method to develop an Arabic-free tokenization language model. In this research, we will precisely evaluate our model’s performance across a range of eight tasks using Arabic Language Understanding Evaluation (ALUE) benchmark. Furthermore, we will conduct a comparative analysis, pitting our free-tokenization model against existing Arabic language models that rely on sub-word tokenization. By making our pre-training and fine-tuning models accessible to the Arabic NLP community, we aim to facilitate the replication of our experiments and contribute to the advancement of Arabic language processing capabilities. To further support reproducibility and open-source collaboration, the complete source code and model checkpoints will be made publicly available on our Huggingface1 . In conclusion, the results of our study will demonstrate that the free-tokenization approach exhibits comparable performance to established Arabic language models that utilize sub-word tokenization techniques. Notably, in certain tasks, our model surpasses the performance of some of these existing models. This evidence underscores the efficacy of free-tokenization in processing the Arabic language, particularly in specific linguistic contexts.
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
A NOVEL APPROACH FOR NAMED ENTITY RECOGNITION ON HINDI LANGUAGE USING RESIDUA...kevig
Many Natural Language Processing (NLP) applications involve Named Entity Recognition (NER) as an important task, where it leads to improve the overall performance of NLP applications. In this paper the Deep learning techniques are used to perform NER task on Hindi text data as it found that as compared to English NER, Hindi language NER is not sufficiently done. This is a barrier for resource-scarce languages as many resources are not readily available. Many researchers use various techniques such as rule based, machine learning based and hybrid approaches to solve this problem. Deep learning based algorithms are being developed in large scale as an innovative approach now a days for the advanced NER models which will give the best results out of it. In this paper we devise a Novel architecture based on residual network architecture for preferably Bidirectional Long Short Term Memory (BiLSTM) with fasttext word embedding layers. For this purpose we use pre-trained word embedding to represent the words in the corpus where the NER tags of the words are defined as the used annotated corpora. BiLSTM Development of an NER system for Indian languages is a comparatively difficult task. In this paper, we have done the various experiments to compare the results of NER with normal embedding and fasttext embedding layers to analyse the performance of word embedding with different batch sizes to train the deep learning models. Here we present a state-of-the-art results with said approach F1 Score measures.
GPT stands for Generative Pre-trained Transformer, the first generalized language model in NLP. Previously, language models were only designed for single tasks like text generation, summarization or classification.
Arabic named entity recognition using deep learning approachIJECEIAES
Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
Local Applications of Large Language Models based on RAG.pptxlwz614595250
We present an approach. We can deploy large models locally in an efficiently realizable way. Targeting some domain-specific applications will help a lot.
Improving the role of language model in statistical machine translation (Indo...IJECEIAES
The statistical machine translation (SMT) is widely used by researchers and practitioners in recent years. SMT works with quality that is determined by several important factors, two of which are language and translation model. Research on improving the translation model has been done quite a lot, but the problem of optimizing the language model for use on machine translators has not received much attention. On translator machines, language models usually use trigram models as standard. In this paper, we conducted experiments with four strategies to analyze the role of the language model used in the Indonesian-Javanese translation machine and show improvement compared to the baseline system with the standard language model. The results of this research indicate that the use of 3-gram language models is highly recommended in SMT.
ADVERSARIAL GRAMMATICAL ERROR GENERATION: APPLICATION TO PERSIAN LANGUAGEkevig
Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data.
However, the preparation of a large amount of labelled training data is time-consuming and prone to
human errors. These issues have become major obstacles in training GEC systems. Recently, the
performance of English GEC systems has drastically been enhanced by the application of deep neural
networks that generate a large amount of synthetic data from limited samples. While GEC has extensively
been studied in languages such as English and Chinese, no attempts have been made to generate synthetic
data for improving Persian GEC systems. Given the substantial grammatical and semantic differences of
the Persian language, in this paper, we propose a new deep learning framework to create large enough
synthetic sentences that are grammatically incorrect for training Persian GEC systems. A modified version
of sequence generative adversarial net with policy gradient is developed, in which the size of the model is
scaled down and the hyperparameters are tuned. The generator is trained in an adversarial framework on
a limited dataset of 8000 samples. Our proposed adversarial framework achieved bilingual evaluation
understudy (BLEU) scores of 64.5% on BLEU-2, 44.2% on BLEU-3, and 21.4% on BLEU-4, and
outperformed the conventional supervised-trained long short-term memory using maximum likelihood
estimation and recently proposed sequence labeler using neural machine translation augmentation. This
shows promise toward improving the performance of GEC systems by generating a large amount of
training data.
ADVERSARIAL GRAMMATICAL ERROR GENERATION: APPLICATION TO PERSIAN LANGUAGEkevig
Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data.
However, the preparation of a large amount of labelled training data is time-consuming and prone to
human errors. These issues have become major obstacles in training GEC systems. Recently, the
performance of English GEC systems has drastically been enhanced by the application of deep neural
networks that generate a large amount of synthetic data from limited samples. While GEC has extensively
been studied in languages such as English and Chinese, no attempts have been made to generate synthetic
data for improving Persian GEC systems. Given the substantial grammatical and semantic differences of
the Persian language, in this paper, we propose a new deep learning framework to create large enough
synthetic sentences that are grammatically incorrect for training Persian GEC systems. A modified version
of sequence generative adversarial net with policy gradient is developed, in which the size of the model is
scaled down and the hyperparameters are tuned. The generator is trained in an adversarial framework on
a limited dataset of 8000 samples. Our proposed adversarial framework achieved bilingual evaluation
understudy (BLEU) scores of 64.5% on BLEU-2, 44.2% on BLEU-3, and 21.4% on BLEU-4, and
outperformed the conventional supervised-trained long short-term memory using maximum likelihood
estimation and recently proposed sequence labeler using neural machine translation augmentation. This
shows promise toward improving the performance of GEC systems by generating a large amount of
training data.
A prior case study of natural language processing on different domain IJECEIAES
In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
This paper presents machine translation based on machine learning, which learns the semantically
correct corpus. The machine learning process based on Quantum Neural Network (QNN) is used to
recognizing the corpus pattern in realistic way. It translates on the basis of knowledge gained during
learning by entering pair of sentences from source to target language. By taking help of this training data
it translates the given text. own text.The paper consist study of a machine translation system which
converts source language to target language using quantum neural network. Rather than comparing
words semantically QNN compares numerical tags which is faster and accurate. In this tagger tags the
part of sentences discretely which helps to map bilingual sentences.
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
A NOVEL APPROACH FOR NAMED ENTITY RECOGNITION ON HINDI LANGUAGE USING RESIDUA...kevig
Many Natural Language Processing (NLP) applications involve Named Entity Recognition (NER) as an important task, where it leads to improve the overall performance of NLP applications. In this paper the Deep learning techniques are used to perform NER task on Hindi text data as it found that as compared to English NER, Hindi language NER is not sufficiently done. This is a barrier for resource-scarce languages as many resources are not readily available. Many researchers use various techniques such as rule based, machine learning based and hybrid approaches to solve this problem. Deep learning based algorithms are being developed in large scale as an innovative approach now a days for the advanced NER models which will give the best results out of it. In this paper we devise a Novel architecture based on residual network architecture for preferably Bidirectional Long Short Term Memory (BiLSTM) with fasttext word embedding layers. For this purpose we use pre-trained word embedding to represent the words in the corpus where the NER tags of the words are defined as the used annotated corpora. BiLSTM Development of an NER system for Indian languages is a comparatively difficult task. In this paper, we have done the various experiments to compare the results of NER with normal embedding and fasttext embedding layers to analyse the performance of word embedding with different batch sizes to train the deep learning models. Here we present a state-of-the-art results with said approach F1 Score measures.
GPT stands for Generative Pre-trained Transformer, the first generalized language model in NLP. Previously, language models were only designed for single tasks like text generation, summarization or classification.
Arabic named entity recognition using deep learning approachIJECEIAES
Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
Local Applications of Large Language Models based on RAG.pptxlwz614595250
We present an approach. We can deploy large models locally in an efficiently realizable way. Targeting some domain-specific applications will help a lot.
Improving the role of language model in statistical machine translation (Indo...IJECEIAES
The statistical machine translation (SMT) is widely used by researchers and practitioners in recent years. SMT works with quality that is determined by several important factors, two of which are language and translation model. Research on improving the translation model has been done quite a lot, but the problem of optimizing the language model for use on machine translators has not received much attention. On translator machines, language models usually use trigram models as standard. In this paper, we conducted experiments with four strategies to analyze the role of the language model used in the Indonesian-Javanese translation machine and show improvement compared to the baseline system with the standard language model. The results of this research indicate that the use of 3-gram language models is highly recommended in SMT.
ADVERSARIAL GRAMMATICAL ERROR GENERATION: APPLICATION TO PERSIAN LANGUAGEkevig
Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data.
However, the preparation of a large amount of labelled training data is time-consuming and prone to
human errors. These issues have become major obstacles in training GEC systems. Recently, the
performance of English GEC systems has drastically been enhanced by the application of deep neural
networks that generate a large amount of synthetic data from limited samples. While GEC has extensively
been studied in languages such as English and Chinese, no attempts have been made to generate synthetic
data for improving Persian GEC systems. Given the substantial grammatical and semantic differences of
the Persian language, in this paper, we propose a new deep learning framework to create large enough
synthetic sentences that are grammatically incorrect for training Persian GEC systems. A modified version
of sequence generative adversarial net with policy gradient is developed, in which the size of the model is
scaled down and the hyperparameters are tuned. The generator is trained in an adversarial framework on
a limited dataset of 8000 samples. Our proposed adversarial framework achieved bilingual evaluation
understudy (BLEU) scores of 64.5% on BLEU-2, 44.2% on BLEU-3, and 21.4% on BLEU-4, and
outperformed the conventional supervised-trained long short-term memory using maximum likelihood
estimation and recently proposed sequence labeler using neural machine translation augmentation. This
shows promise toward improving the performance of GEC systems by generating a large amount of
training data.
ADVERSARIAL GRAMMATICAL ERROR GENERATION: APPLICATION TO PERSIAN LANGUAGEkevig
Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data.
However, the preparation of a large amount of labelled training data is time-consuming and prone to
human errors. These issues have become major obstacles in training GEC systems. Recently, the
performance of English GEC systems has drastically been enhanced by the application of deep neural
networks that generate a large amount of synthetic data from limited samples. While GEC has extensively
been studied in languages such as English and Chinese, no attempts have been made to generate synthetic
data for improving Persian GEC systems. Given the substantial grammatical and semantic differences of
the Persian language, in this paper, we propose a new deep learning framework to create large enough
synthetic sentences that are grammatically incorrect for training Persian GEC systems. A modified version
of sequence generative adversarial net with policy gradient is developed, in which the size of the model is
scaled down and the hyperparameters are tuned. The generator is trained in an adversarial framework on
a limited dataset of 8000 samples. Our proposed adversarial framework achieved bilingual evaluation
understudy (BLEU) scores of 64.5% on BLEU-2, 44.2% on BLEU-3, and 21.4% on BLEU-4, and
outperformed the conventional supervised-trained long short-term memory using maximum likelihood
estimation and recently proposed sequence labeler using neural machine translation augmentation. This
shows promise toward improving the performance of GEC systems by generating a large amount of
training data.
A prior case study of natural language processing on different domain IJECEIAES
In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
This paper presents machine translation based on machine learning, which learns the semantically
correct corpus. The machine learning process based on Quantum Neural Network (QNN) is used to
recognizing the corpus pattern in realistic way. It translates on the basis of knowledge gained during
learning by entering pair of sentences from source to target language. By taking help of this training data
it translates the given text. own text.The paper consist study of a machine translation system which
converts source language to target language using quantum neural network. Rather than comparing
words semantically QNN compares numerical tags which is faster and accurate. In this tagger tags the
part of sentences discretely which helps to map bilingual sentences.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
BERT Explained_ State of the art language model for NLP.pdf
1. 4/19/23, 1:13 AM BERT Explained: State of the art language model for NLP | byRani Horev| Towards Data Science
https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 1/13
Published in Towards Data Science
Rani Horev Follow
Nov 10, 2018 · 7 min read · Listen
BERTExplained: State of the art language
modelfor NLP
BERT (Bidirectional Encoder Representations from Transformers) is a recent
paper published by researchers at Google AI Language. It has caused a stir
in the Machine Learning community by presenting state-of-the-art results in
a wide variety of NLP tasks, including Question Answering (SQuAD v1.1),
Natural Language Inference (MNLI), and others.
BERT’s key technical innovation is applying the bidirectional training of
Transformer, a popular attention model, to language modelling. This is in
contrast to previous efforts which looked at a text sequence either from left
to right or combined left-to-right and right-to-left training. The paper’s results
show that a language model which is bidirectionally trained can have a
9K 29
Search Medium Write
2. 4/19/23, 1:13 AM BERT Explained: State of the art language model for NLP | byRani Horev| Towards Data Science
https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 2/13
deeper sense of language context and flow than single-direction language
models. In the paper, the researchers detail a novel technique named
Masked LM (MLM) which allows bidirectional training in models in which it
was previously impossible.
Background
In the field of computer vision, researchers have repeatedly shown the value
of transfer learning — pre-training a neural network model on a known task,
for instance ImageNet, and then performing fine-tuning — using the trained
neural network as the basis of a new purpose-specific model. In recent
years, researchers have been showing that a similar technique can be useful
in many natural language tasks.
A different approach, which is also popular in NLP tasks and exemplified in
the recent ELMo paper, is feature-based training. In this approach, a pre-
trained neural network produces word embeddings which are then used as
features in NLP models.
How BERT works
3. 4/19/23, 1:13 AM BERT Explained: State of the art language model for NLP | byRani Horev| Towards Data Science
https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 3/13
BERT makes use of Transformer, an attention mechanism that learns
contextual relations between words (or sub-words) in a text. In its vanilla
form, Transformer includes two separate mechanisms — an encoder that
reads the text input and a decoder that produces a prediction for the task.
Since BERT’s goal is to generate a language model, only the encoder
mechanism is necessary. The detailed workings of Transformer are
described in a paper by Google.
As opposed to directional models, which read the text input sequentially
(left-to-right or right-to-left), the Transformer encoder reads the entire
sequence of words at once. Therefore it is considered bidirectional, though it
would be more accurate to say that it’s non-directional. This characteristic
allows the model to learn the context of a word based on all of its
surroundings (left and right of the word).
The chart below is a high-level description of the Transformer encoder. The
input is a sequence of tokens, which are first embedded into vectors and
then processed in the neural network. The output is a sequence of vectors of
size H, in which each vector corresponds to an input token with the same
index.
4. 4/19/23, 1:13 AM BERT Explained: State of the art language model for NLP | byRani Horev| Towards Data Science
https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 4/13
When training language models, there is a challenge of defining a prediction
goal. Many models predict the next word in a sequence (e.g. “The child came
home from ___”), a directional approach which inherently limits context
learning. To overcome this challenge, BERT uses two training strategies:
Masked LM (MLM)
Before feeding word sequences into BERT, 15% of the words in each
sequence are replaced with a [MASK] token. The model then attempts to
predict the original value of the masked words, based on the context
provided by the other, non-masked, words in the sequence. In technical
terms, the prediction of the output words requires:
1. Adding a classification layer on top of the encoder output.
2. Multiplying the output vectors by the embedding matrix, transforming
them into the vocabulary dimension.
3. Calculating the probability of each word in the vocabulary with softmax.
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The BERT loss function takes into consideration only the prediction of the
masked values and ignores the prediction of the non-masked words. As a
consequence, the model converges slower than directional models, a
characteristic which is offset by its increased context awareness (see
Takeaways #3).
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Note: In practice, the BERT implementation is slightly more elaborate and doesn’t
replace all of the 15% masked words. See Appendix A for additional information.
Next Sentence Prediction (NSP)
In the BERT training process, the model receives pairs of sentences as input
and learns to predict if the second sentence in the pair is the subsequent
sentence in the original document. During training, 50% of the inputs are a
pair in which the second sentence is the subsequent sentence in the original
document, while in the other 50% a random sentence from the corpus is
chosen as the second sentence. The assumption is that the random sentence
will be disconnected from the first sentence.
To help the model distinguish between the two sentences in training, the
input is processed in the following way before entering the model:
1. A [CLS] token is inserted at the beginning of the first sentence and a [SEP]
token is inserted at the end of each sentence.
2. A sentence embedding indicating Sentence A or Sentence B is added to
each token. Sentence embeddings are similar in concept to token
embeddings with a vocabulary of 2.
3. A positional embedding is added to each token to indicate its position in
the sequence. The concept and implementation of positional embedding
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are presented in the Transformer paper.
Source: BERT [Devlin et al., 2018], with modifications
To predict if the second sentence is indeed connected to the first, the
following steps are performed:
1. The entire input sequence goes through the Transformer model.
2. The output of the [CLS] token is transformed into a 2×1 shaped vector,
using a simple classification layer (learned matrices of weights and
biases).
3. Calculating the probability of IsNextSequence with softmax.
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When training the BERT model, Masked LM and Next Sentence Prediction
are trained together, with the goal of minimizing the combined loss function
of the two strategies.
How to use BERT (Fine-tuning)
Using BERT for a specific task is relatively straightforward:
BERT can be used for a wide variety of language tasks, while only adding a
small layer to the core model:
1. Classification tasks such as sentiment analysis are done similarly to Next
Sentence classification, by adding a classification layer on top of the
Transformer output for the [CLS] token.
2. In Question Answering tasks (e.g. SQuAD v1.1), the software receives a
question regarding a text sequence and is required to mark the answer in
the sequence. Using BERT, a Q&A model can be trained by learning two
extra vectors that mark the beginning and the end of the answer.
3. In Named Entity Recognition (NER), the software receives a text
sequence and is required to mark the various types of entities (Person,
Organization, Date, etc) that appear in the text. Using BERT, a NER model
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can be trained by feeding the output vector of each token into a
classification layer that predicts the NER label.
In the fine-tuning training, most hyper-parameters stay the same as in BERT
training, and the paper gives specific guidance (Section 3.5) on the hyper-
parameters that require tuning. The BERT team has used this technique to
achieve state-of-the-art results on a wide variety of challenging natural
language tasks, detailed in Section 4 of the paper.
Takeaways
1. Model size matters, even at huge scale. BERT_large, with 345 million
parameters, is the largest model of its kind. It is demonstrably superior
on small-scale tasks to BERT_base, which uses the same architecture
with “only” 110 million parameters.
2. With enough training data, more training steps == higher accuracy. For
instance, on the MNLI task, the BERT_base accuracy improves by 1.0%
when trained on 1M steps (128,000 words batch size) compared to 500K
steps with the same batch size.
3. BERT’s bidirectional approach (MLM) converges slower than left-to-right
approaches (because only 15% of words are predicted in each batch) but
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bidirectional training still outperforms left-to-right training after a small
number of pre-training steps.
Source: BERT [Devlin et al., 2018]
Compute considerations (training and applying)
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Conclusion
BERT is undoubtedly a breakthrough in the use of Machine Learning for
Natural Language Processing. The fact that it’s approachable and allows fast
fine-tuning will likely allow a wide range of practical applications in the
future. In this summary, we attempted to describe the main ideas of the
paper while not drowning in excessive technical details. For those wishing
for a deeper dive, we highly recommend reading the full article and ancillary
articles referenced in it. Another useful reference is the BERT source code
and models, which cover 103 languages and were generously released as
open source by the research team.
Appendix A — Word Masking
Training the language model in BERT is done by predicting 15% of the
tokens in the input, that were randomly picked. These tokens are pre-
processed as follows — 80% are replaced with a “[MASK]” token, 10% with a
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random word, and 10% use the original word. The intuition that led the
authors to pick this approach is as follows (Thanks to Jacob Devlin from
Google for the insight):
If we used [MASK] 100% of the time the model wouldn’t necessarily
produce good token representations for non-masked words. The non-
masked tokens were still used for context, but the model was optimized
for predicting masked words.
If we used [MASK] 90% of the time and random words 10% of the time,
this would teach the model that the observed word is never correct.
If we used [MASK] 90% of the time and kept the same word 10% of the
time, then the model could just trivially copy the non-contextual
embedding.
No ablation was done on the ratios of this approach, and it may have worked
better with different ratios. In addition, the model performance wasn’t tested
with simply masking 100% of the selected tokens.
For more summaries on the recent Machine Learning research, check out Lyrn.AI.
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