Whether you work in business, healthcare, education or any other industry
Our IR technology will help you save time and improve work efficiency
Our Technology Have
TF (Term Frequency)
Count how many the number of times a word appears in a document
IDF (Inverse Document Frequency)
Count how many documents that word appears in that
TF-IDF (Term Frequency-Inverse Document Frequency)
Combine both TF and IDF to calculate word importance scores in documents
TF-IDF is widely used in search systems. The system will determine which words are of most interest to the user, thereby returning the results exactly as the user desires.
Cosine Similarity
Measures the similarity between two documents by comparing the angle between two vectors representing those documents
Categorize, extraction and retrieve information and text
Measures the similarity between a user's query and the documents in the database
Classify documents into different categories
Identify sentences or paragraphs that relate to a particular topic
Image Processing
In image processing, image features (such as color histograms or feature vectors extracted from deep learning models) can be compared using Cosine Similarity to find similar images.
Document Comparison
Compare and identify similarities between two documents. This is useful in applications such as plagiarism checking, where it is necessary to compare new text with a database of existing texts to detect copied text.
Content Recommendation
Recommendation systems use Cosine Similarity to find similar products or content based on user preferences
Cosine Similarity is a powerful and flexible tool in many different applications, helping to measure and exploit similarities between objects in high-dimensional space.
Question Answering: BERT is used in systems that extract answers from a body of text, like Google's search engine and various customer service bots.
Sentiment Analysis: Businesses use BERT to analyze customer reviews, social media comments, and other text data to gauge public sentiment towards their products or services.
Named Entity Recognition (NER): BERT helps in identifying and classifying entities (like names of people, organizations, dates) in text.
Text Classification: It is used to classify texts into different categories, such as spam detection in emails, news categorization, and more.
Bidirectional Context
Unlike previous models that read text sequentially (left-to-right or right-to-left), BERT reads text in both directions simultaneously. This bidirectional approach allows BERT to understand the context of a word based on its surrounding words from both sides.
Transformer Architecture
BERT is built on the Transformer architecture, which uses self-attention mechanisms to weigh the importance of different words in a sentence. This helps BERT capture complex relationships between words.
Pre-training
BERT is pre-trained on large text corpora using two tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). In MLM, some words in a sentence are masked, and the model learns to predict them. In NSP, the model learns to predict if two sentences follow each other.
Fine-tuning
After pre-training, BERT can be fine-tuned on specific tasks like question answering, sentiment analysis, or named entity recognition. This involves training the model on a smaller, task-specific dataset.
State-of-the-Art Performance
BERT has set new benchmarks for a wide range of NLP tasks. It has significantly improved performance in tasks like question answering, text classification, and language inference.
Versions of BERT
BERT has several versions, including BERT-Base and BERT-Large. BERT-Base has 12 layers (transformer blocks) with 110 million parameters, while BERT-Large has 24 layers with 340 million parameters.
BERT has significantly advanced the field of NLP by providing a powerful tool for understanding and generating human language. Its bidirectional approach and transformer architecture allow it to capture the nuanced context of words, making it highly effective for various language tasks.