Fine-tune DistilBert on JSONL with Manual Encoding
Generates a Python script to fine-tune a DistilBert model on a JSONL dataset containing 'question' and 'answer' columns. The script uses manual label mapping (avoiding sklearn), includes progress logging, error handling, and model evaluation.
npx skills add ECNU-ICALK/AutoSkill --skill fine-tune-distilbert-on-jsonl-with-manual-encoding --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Fine-tune DistilBert on JSONL with Manual Encoding Generates a Python script to fine-tune a DistilBert model on a JSONL dataset containing 'question' and 'answer' columns. The script uses manual label mapping (avoiding sklearn), includes progress logging, error handling, and model evaluation. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in the Hugging Face Transformers library. Your task is to generate a complete, executable Python script to fine-tune a DistilBert model on a user-provided JSONL dataset. # Communication & Style Preferences - Provide clear, executable Python code blocks. - Use comments to explain key steps in the code. - Ensure the code is robust and follows best practices for PyTorch and Transformers. # Operational Rules & Constraints 1. **Dataset Handling**: The input dataset is a JSONL file with two columns: 'question' and 'answer'. Use the `datasets` library to load it. 2. **Label Encoding**: Do NOT use `sklearn` or `LabelEncoder`. You must manually extract unique answers, create a dictionary mapping (`answer_to_id`), and map the answers to integer IDs using a custom function and `dataset.map`. 3. **Model Loading**: Load `Distil
- Prompt
- Triggers
What does the Fine-tune DistilBert on JSONL with Manual Encoding skill do?
Generates a Python script to fine-tune a DistilBert model on a JSONL dataset containing 'question' and 'answer' columns. The script uses manual label mapping (avoiding sklearn), includes progress logging, error handling, and model evaluation.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill fine-tune-distilbert-on-jsonl-with-manual-encoding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.