aitraining text-classification \
  --model bert-base-uncased \
  --data-path ./reviews.csv \
  --text-column text \
  --target-column label \
  --project-name sentiment-model

aitraining text-classification \
  --model distilbert-base-uncased \
  --data-path ./sentiment.csv \
  --text-column review \
  --target-column sentiment \
  --project-name sentiment \
  --epochs 5 \
  --batch-size 16

aitraining text-regression \
  --model bert-base-uncased \
  --data-path ./scores.csv \
  --text-column text \
  --target-column score \
  --project-name score-predictor

aitraining text-regression \
  --model microsoft/deberta-v3-base \
  --data-path ./reviews.csv \
  --text-column review_text \
  --target-column rating \
  --project-name rating-predictor \
  --epochs 10

aitraining token-classification \
  --model bert-base-cased \
  --data-path ./ner_data.json \
  --tokens-column tokens \
  --tags-column ner_tags \
  --project-name ner-model

{
  "tokens": ["John", "lives", "in", "New", "York"],
  "ner_tags": ["B-PER", "O", "O", "B-LOC", "I-LOC"]
}

aitraining token-classification \
  --model bert-base-cased \
  --data-path ./custom_entities.json \
  --tokens-column words \
  --tags-column labels \
  --project-name custom-ner \
  --epochs 5 \
  --batch-size 16

aitraining seq2seq \
  --model t5-small \
  --data-path ./translations.csv \
  --text-column source \
  --target-column target \
  --project-name translator

aitraining seq2seq \
  --model facebook/bart-base \
  --data-path ./articles.csv \
  --text-column article \
  --target-column summary \
  --project-name summarizer \
  --epochs 3 \
  --max-seq-length 1024 \
  --max-target-length 128

aitraining extractive-qa \
  --model bert-base-uncased \
  --data-path ./squad_format.json \
  --project-name qa-model

{
  "context": "Paris is the capital of France.",
  "question": "What is the capital of France?",
  "answers": {
    "text": ["Paris"],
    "answer_start": [0]
  }
}

aitraining sentence-transformers \
  --model sentence-transformers/all-MiniLM-L6-v2 \
  --data-path ./pairs.csv \
  --project-name embeddings

sentence1,sentence2,score
"The cat sits.",The feline rests.",0.9
"I love pizza","The sky is blue",0.1