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name: training description: "Prepare BEIR training pairs and triplets, build evaluators, select losses, and plan safe SentenceTransformers training runs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

BEIR Training

Use this sub-skill when the task is to prepare or validate BEIR/SentenceTransformers training inputs, construct TrainRetriever data loaders and evaluators, choose BEIR-compatible losses, or plan a safe training invocation.

Route

• Start with references/workflows.md for pair/triplet preparation, hard-negative patterns, evaluator setup, and safe training planning.
• Use references/api-reference.md for TrainRetriever, BPRLoss, MarginMSELoss, SentenceTransformers loss choices, and fit parameters.
• Use references/troubleshooting.md for qrels/corpus mismatches, empty dev sets, max_corpus_size, triplet shape, dependency compatibility, and expensive training failures.

Bundled Helper

• Run a no-download training data preflight: python scripts/training_data_smoke.py
• Check the max_corpus_size guard explicitly: python scripts/training_data_smoke.py --exercise-max-corpus-error

The helper imports BEIR training classes, uses tiny in-memory data, validates InputExample creation, checks triplet shape expectations, constructs a tiny IR evaluator, and exercises common failure modes without downloading a model or running training.

Boundaries

• This sub-skill owns beir.retrieval.train.TrainRetriever, beir.losses.BPRLoss, beir.losses.MarginMSELoss, training pair/triplet conversion, evaluator construction for training callbacks, optimizer/scheduler defaults, and training safety planning.
• Route dataset file layout, loader use, and BEIR-format validation to ../data-loading/SKILL.md.
• Route generated synthetic queries or passage expansion to ../generation/SKILL.md.
• Route final retrieval runs and metric reporting after a checkpoint is trained to ../retrieval-evaluation/SKILL.md.
• Route second-stage cross-encoder reranking to ../reranking/SKILL.md.