---

name: training-finetuning description: "Route supervised and contrastive fine-tuning for CoSENT, Sentence-BERT, BERT-match, and BGE, including dataset schemas, validation helpers, command construction, and GPU/multi-card boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Fine-tuning

Use this sub-skill for text-matching and retrieval fine-tuning workflows:

• CoSENT
• Sentence-BERT
• BERT-match
• BGE

Use the bundled validators before training custom data.

Route away

• embeddings-only inference → embeddings
• benchmark reporting or model choice → evaluation-benchmarks
• serving or deployment → serving-deployment

Read in order

1. references/data-formats.md
2. references/api-reference.md
3. references/training-workflows.md
4. references/troubleshooting.md

Bundled scripts

• scripts/validate_text_matching_data.py
• scripts/validate_bge_jsonl.py

Operating notes

• Full training may need network access, GPU hardware, bf16 support, or Hugging Face dataset cache.
• Keep output_dir dedicated to one experiment; it is the reload path for prediction.
• Do not assume field names are uniform: the loaders accept either sentence1/sentence2 or text1/text2.