Fine-tune DistilBert on JSONL Dataset

Fine-tune DistilBert on JSONL Dataset is a Model training & evaluation skill for AI agents, published by THUIR in the MemoryBench repository on GitHub. Skillspace rates Fine-tune DistilBert on JSONL Dataset C (73/100) and it passed the safety scan; the rating is based on static analysis only.

Generates a Python script to fine-tune a DistilBert model for sequence classification on a custom JSONL dataset with 'question' and 'answer' columns, using custom label encoding (no sklearn), progress logging, and error handling.

Grade
C
Quality
73 / 100
Safety
Passed
GitHub stars
84

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubTHUIR/MemoryBench

Our review

Architecture
97 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
MIT
Adoption
Adopted by 0 other repositories, in 2 collections

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

More Model training & evaluation skills

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.