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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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ECNU-ICALK/AutoSkill --skill fine-tune-distilbert-on-jsonl-dataset명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
SOC 직업 분류 기준
Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
布置结构化家庭作业，引导求助者记录现实互动事件、自动思维及情绪反应，并设计简易验证行动（如主动询问、观察反证），用于检验投射性认知偏差。适用于已识别出具体非理性信念（如‘别人肯定不喜欢我’）且情绪稳定者。
结构化8次CBT咨询流程，按评估性会谈→咨询性会谈→巩固性会谈三阶段推进，整合悬搁接地技术与认知行为策略，专用于强迫症伴轻度抑郁状态、具自省力与作业执行力的成年来访者。
| id | 022a8fee-1277-4874-8f3a-a5ff946a3228 |
| name | Fine-tune DistilBert on JSONL Dataset |
| description | 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. |
| version | 0.1.0 |
| tags | ["distilbert","finetuning","huggingface","jsonl","python","machine-learning"] |
| triggers | ["finetune distilbert on jsonl","train distilbert on custom dataset","code to finetune model on question answer pairs","distilbert classification script without sklearn"] |
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.
You are a Machine Learning Engineer. Write a Python script to fine-tune a DistilBert model on a custom JSONL dataset for a sequence classification task.
transformers, datasets, and torch. Do not use sklearn.DistilBertForSequenceClassification from 'distilbert-base-uncased'.answer_to_id = {answer: idx for idx, answer in enumerate(unique_answers)}.DistilBertTokenizerFast. Tokenize the 'question' column with padding='max_length' and truncation=True.Trainer API.TrainingArguments with output_dir='./results', num_train_epochs=2, per_device_train_batch_size=32, evaluation_strategy='epoch', save_strategy='epoch', load_best_model_at_end=True, and logging_dir='./logs'.num_labels equal to the number of unique answers.try...except block to catch and print exceptions.sklearn.preprocessing.LabelEncoder.