Databricks Partner

With the significant growth in SaaS LLM APIs usage and ML applications, dbt has emerged as the most rapidly expanding tool, indicating a surge in efficient data manipulation within the Databricks Lakehouse. The shift towards Delta Lake from on-prem and cloud data warehouses signifies an industry move towards more efficient data science (DS) methodologies aimed at accelerating growth, improving predictability, and enhancing customer experiences.

Our expertise spans a range of DS/ML Applications, such as Speech Recognition, Simulations & Optimizations, Recommender Systems, Natural Language Processing, and Industry Data Modeling. By employing specialized Python libraries including NLTK, Transformers, and FuzzyWuzzy, as well as Transformer-related libraries and LLM tools like LangChain, we tackle complex DS/ML projects. The use of the MLflow Tracking Server and MLflow Model Registry is central to our approach in managing project tracking and model lifecycle.

At Bitext, our collaboration with Databrick goes beyond data integration and business intelligence (BI). It’s about leveraging the latest in data science and machine learning to offer solutions that are not just successful but also sustainable, ensuring our clients can efficiently adapt to new technologies and methodologies in AI and ML.

Consulting

Bitext Builds Datasets for Fine-tuning LLMs

Our Data Preparation Solution Has Two Components

1. We Leverage Your Internal Data Sources

Data Collection

We help you identify and collect high-quality datasets that align with your specific application and domain. Our team of experts assists in sourcing diverse and relevant data, ensuring that you have a robust foundation for training your LLM.

Annotation and Labeling

If your LLM requires annotated or labelled data, we offer efficient annotation services. Our experienced annotators precisely label the data based on your specific requirements, whether it’s sentiment analysis, named entity recognition, or any other custom annotation task.

2. We Expand Your Internal Data with Synthetic Text (NLG)

Privacy and Compliance

We understand the importance of data privacy and compliance. Rest assured that your data will be handled with the utmost confidentiality and in compliance with applicable data protection regulations.

Collaboration and Support

Our dedicated team of data scientists and engineers collaborate closely with you throughout the data preparation process. We provide guidance, support, and expertise to ensure that your data is prepared to maximize the performance of your LLM.

List of Fine-Tuning LLM Verticals

At Bitext, we understand that specialization and adaptability are essential for the seamless operation of automated customer support services. That’s why we are dedicated to fine-tuning large language models (LLMs) to deliver precise, industry-tailored results. Regardless of whether you’re in the automotive sector, academia, or even the intricate world of healthcare, Bitext has specialized datasets to meet the specific needs of any vertical.

We meticulously cater to each industry to facilitate understanding and improve responses to the most common inquiries. By integrating our vertical datasets, we ensure that your customer support systems are equipped to interact with and satisfy a wide array of linguistic demands. Simulating linguistic variations and common writing errors also contributes to the resilience of your system against the unpredictability of everyday language.

We encourage you to explore our range of verticals and download our datasets for evaluation. Learn more about us and discover how vertical-specific data optimization can strengthen the effectiveness of your customer support systems.

Bitext’s LLM Evaluation Methodology for Conversational AI

Overview

Our main advantage is that Bitext automates most steps in the evaluation pipeline, including the generation of an evaluation dataset, which is a critical step in the absence of historical evaluation data.

This semi-supervised process is based on standard accuracy metrics (like the F1-score, that takes into account both precision and recall together). The analysis of these metrics is then compiled in a report highlighting strengths and weaknesses, both at the bot level and at the intent level.

The process combines software tools, evaluation data and expert insights in one single methodology. This methodology is transparent and easy to explain to end users.

The Evaluation Dataset for Fine-tuning LLMs

Data & Flags

The Evaluation Methodology

Datasets

Pre-Built Datasets to train your LLMs

Datasets Available

Our Prebuilt QA datasets are designed to deal with language register variations including polite/formal, colloquial and offensive language. We have profiled the language register use in user queries from a wide range of business sectors, and we use this information to generate training data with a similar profile, ensuring maximum linguistic coverage.

We also introduce noise into the training data, including spelling mistakes, run-on words and missing punctuation. This realistic data makes our Prebuilt Datasets more resilient in the face of “noisy” input that is common in real life.

How did we select these intents?

1. Select a representative set of texts about the domain

3. Normalize frequent actions by analyzing vertical-specific synonyms: “purchase + item” and “buy + product” can be normalized under “purchase + product”

5. We offer to curate a custom ontology specific for each client/chatbot

Bitext NLP Data Overview

Bitext’s Deep Linguistic Analysis

Lexical Level and Lemmatization

The lemmatizer can be additionally packaged to cover the full pipeline of language analysis, from sentence segmentation to full parsing, and includes tools like spell-checking.

Both components of the lemmatizer, data and software, can be distributed integrated or separately. All these tools are available in 77 languages and 25 language variants.

Syntactic Level and Parsing

For a full list of services, at the lexical, syntactic and semantic levels, check our linguistic services.

Example of dataset for Customer Service