The Fifth Elephant 2023 Monsoon

On AI, industrial applications of ML, and MLOps



Samik Raychaudhuri


Shub Jain


Tuning a base language model for multi-tasking

Submitted Jun 30, 2023


Auquan is an AI startup that serves institutional investors and investment managers with curated news and documents to help them make better investment decisions.

In this presentation, I will discuss our approach for tuning a base language model for multiple tasks, such as determining noise in streaming news feeds, determining relevance, matching news to topics, and curating relevant documents.

I will walk through the process and pitfalls for tuning the language model for a general use case, including the process and metric for determining performance of the tuned model.


ML Engineers, early stage Data Scientists


  • How to tune a base language model for multiple tasks
  • Existing libraries for tuning language models
  • Best practices and pitfalls for tuning language models

Presentation Outline

  • Introduction
    • About Auquan and our use case
    • Problem description
  • Language models for multi-tasking
    • How we use language models
    • Tuning an LM
  • Using tuned models for embedding
  • Best practices and pitfalls
  • Conclusion/QA


{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Hybrid access (members only)

Hosted by

All about data science and machine learning

Supported by

E2E Cloud is India's first AI hyper scaler, a cloud computing platform providing accelerated cloud-based solutions at maximum optimization and lowest pricing