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How to route Thai or Japanese intents with a small model with Awesome Jev Gallery

Use the Thai and Japanese decision models to score fixed intent labels for local-language messages.

  • Research
  • Intermediate
  • 120 minutes
  • From the README
  • Any agent

Who it is forA product team that receives customer messages in Thai or Japanese and needs to route them to fixed categories.

The problemRouting messages with a general chat model is slow to tune and gives free-form answers instead of fixed label probabilities.

The outcomeA small model that returns one probability per intent label for each message, tested on your own sample.

What you need first

  • A web browser to read the list and the linked repositories
  • Hardware that matches the open model you choose, such as an RTX GPU or Apple Silicon for local runs
  • A decision task with a fixed set of options you want scored

Steps

  1. Open the OpenThai-SystemOne entry for a Thai and English 0.8B model with a 256-way slot head.
  2. Open the sokudan entry for a Japanese 314.6M ModernBERT-ja model that returns choice, score and bool probabilities.
  3. Map your intent labels to a fixed option set for each language.
  4. Test each model on a held-out sample of your own messages.
  5. Check that the top label matches the intent you expect for most messages before routing live traffic.

Prompt for your agent

Using the OpenThai-SystemOne and sokudan entries in https://github.com/OmniJev/awesome-jev-gallery, map <intent labels> to a fixed option set and score a held-out sample of <language> messages. Report the label probabilities for each message. Verify that the labels cover every intent you plan to route.

Caveat. The reported accuracies are on the authors' own held-out sets, so test on messages from your own product before relying on them.

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