Sign Up

Have an account? Sign In Now

Sign In

Forgot Password?

Don't have account, Sign Up Here

Forgot Password

Lost your password? Please enter your email address. You will receive a link and will create a new password via email.

Have an account? Sign In Now

Sorry, you do not have permission to ask a question, You must login to ask a question.

Forgot Password?

Need An Account, Sign Up Here

Please briefly explain why you feel this question should be reported.

Please briefly explain why you feel this answer should be reported.

Please briefly explain why you feel this user should be reported.

Technomantic Logo Technomantic Logo
Sign InSign Up

Technomantic

Search
Ask A Question

Mobile menu

Close
Ask A Question
  • Home
  • Add group
  • Groups page
  • Feed
  • User Profile
  • Communities
  • Questions
    • New Questions
    • Trending Questions
    • Must read Questions
    • Hot Questions
  • Polls
  • Tags
  • Badges
  • Users
  • Help
Home/ Questions/Q 21696
Next
In Process

Technomantic Latest Questions

BryanJohansan
  • 5
  • 5
BryanJohansanBegginer
Asked: May 30, 20252025-05-30T00:35:19+00:00 2025-05-30T00:35:19+00:00In: Natural Language Processing (NLP)

My training loss on my transformer model just won’t settle down, it keeps jumping all over the place. Could this be a learning rate issue or something else?

  • 5
  • 5

I’ve tried lowering the learning rate but no luck. Wondering if batch size or tokenization might be causing this.

ainlp
2
  • 2 2 Answers
  • 48 Views
  • 0 Followers
  • 0
    • Report
  • Share
    Share
    • Share on Facebook
    • Share on Twitter
    • Share on LinkedIn
    • Share on WhatsApp

You must login to add an answer.

Forgot Password?

Need An Account, Sign Up Here

2 Answers

  • Voted
  • Oldest
  • Recent
  • Random
  1. Rundu
    Rundu Begginer
    2025-05-31T01:45:33+00:00Added an answer on May 31, 2025 at 1:45 am

    Yeah, that kind of erratic loss can definitely be frustrating. From what you’re describing, it could be a learning rate issue — that’s often the first thing I’d look at. When the learning rate is too high, the model starts overshooting during optimization, kind of like it’s bouncing around instead of settling into a groove. Lowering it, even just a bit, can sometimes calm things down noticeably.
    But it’s not always that simple. Sometimes the issue isn’t just the learning rate itself, but how it’s changing over time — especially if you’re using a transformer. Those models really like having a learning rate warmup in the beginning and a proper decay afterward. If your schedule’s too aggressive or missing altogether, it could explain the instability.
    Also, not to freak you out, but sometimes the root cause is buried in something like bad input data or tiny batch sizes that make your training super noisy. Even things like not clipping gradients can silently cause chaos behind the scenes.
    If you want to dig deeper, feel free to share a few details like your learning rate, optimizer, and whether you’re using any warmup. Sometimes just tweaking one thing makes a world of difference.

      • 0
    • Share
      Share
      • Share on Facebook
      • Share on Twitter
      • Share on LinkedIn
      • Share on WhatsApp
      • Report
  2. Hassaan Arif
    Hassaan Arif Enlightened
    2025-05-31T13:32:53+00:00Added an answer on May 31, 2025 at 1:32 pm

    If your transformer’s training loss is jumping around, a high learning rate is often to blame.

    Try reducing it to something like 1e-4 or 1e-5 if you’re using Adam. Using a warm-up schedule can also help smooth out the early stages of training.

    Gradient explosions can cause instability too, so it’s worth adding gradient clipping.

    Also check your input data for noise, mislabeled samples, or inconsistent padding these small issues can throw training off.

    Sometimes it’s just about slowing things down and letting the model learn at a steady rhythm.

      • 0
    • Share
      Share
      • Share on Facebook
      • Share on Twitter
      • Share on LinkedIn
      • Share on WhatsApp
      • Report

Sidebar

Ask A Question

Stats

  • Questions 28
  • Answers 53
  • Best Answers 22
  • Users 32
  • Popular
  • Answers
  • Rety1

    How do you decide between using CNNs, RNNs, or Transformers ...

    • 4 Answers
  • Rety1

    I'm facing overfitting issues in my deep learning model. What ...

    • 4 Answers
  • Jiyakhan

    What are the most beginner-friendly tools/platforms to prototype a voice ...

    • 3 Answers
  • Hassaan Arif
    Hassaan Arif added an answer AI can inform emotional decision-making, but it should never replace… June 10, 2025 at 10:07 pm
  • Hassaan Arif
    Hassaan Arif added an answer Human-centered AI” is not just a tech buzzword. It’s about… June 10, 2025 at 10:06 pm
  • Hassaan Arif
    Hassaan Arif added an answer In a world where AI can replicate your voice, writing… June 10, 2025 at 10:04 pm

Related Questions

  • I'm working on a chatbot project. Which NLP libraries have ...

    • 2 Answers

Top Members

Hassaan Arif

Hassaan Arif

  • 0 Questions
  • 5k Points
Enlightened
morila

morila

  • 2 Questions
  • 42 Points
Begginer
Lartax

Lartax

  • 3 Questions
  • 40 Points
Begginer

Trending Tags

ai ai ads creation ai blogs ai lead gen ai tools ai voice agents ai writing banned account chatgpt coding deep learning ml nlp object detection programming quora

Explore

  • Home
  • Groups page
  • Communities
  • Questions
    • New Questions
    • Trending Questions
    • Must read Questions
    • Hot Questions
  • Polls
  • Tags
  • Help

Footer

Technomantic

Technomantic the AI Platform for asking questions, solve AI problems, and connect on machine learning, ChatGPT, NLP, and prompt engineering topics.

About Us

  • About Us
  • Contact Us
  • Contribute

Legal Stuff

  • Disclaimer
  • Privacy Policy
  • Terms and Conditions
  • Community Guidelines / Forum Rules

Help

  • Contact Us

© 2025 Technomantic. All Rights Reserved