judge_answer__22_deberta_v3_large_fresh_repo

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I fine-tuned DeBERTa v3 large on answerability of SQaD v2.

Done here: https://colab.research.google.com/drive/1xAA4D3VkbIXYeyIzn5-PE8Xa1miz9uwq#scrollTo=4G7kLtQiFF7Q

import transformers
tokenized_datasets.set_format('torch')

data_collator = transformers.DataCollatorWithPadding(tokenizer=tokenizer)

# Training arguments
training_args = TrainingArguments(
    run_name=NOTEBOOK_NAME,
    output_dir=NOTEBOOK_NAME,
    learning_rate=1e-5, # 3e-5 seemed bad with deepset/roberta-base-squad2, but not sure...
    per_device_train_batch_size=16,
    gradient_accumulation_steps=2, # others use bs 16, simulate that (2*real-bs)
    weight_decay=0.02,
    num_train_epochs=2,
    fp16=True, # mixed precision training to speed up training and reduce memory usage

    evaluation_strategy="steps",
    eval_steps=500, # prev 1000, but now we have gradient_accumulation_steps=2
    save_strategy="steps",
    save_steps=500,
    save_total_limit=1, # only save latest and best model

    load_best_model_at_end=True,
    metric_for_best_model="f1", # to represent precision-recall tradeoff
    greater_is_better=True,

    report_to=['wandb'],
    logging_steps=500,

    push_to_hub=True,

    warmup_steps=500,  # Add learning rate warm-up
    lr_scheduler_type="linear",  # Use linear decay
    # max_grad_norm=1.0,  # Clip gradients

)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['validation'],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
    data_collator=data_collator, # make batch shorter if possible
    # callbacks=[transformers.EarlyStoppingCallback(early_stopping_patience=3)],
)

trainer.train()

Took around 36gb vram (maybe 38gb). 5538/8144 steps took 1:47:48. Trained on a A100. Cost: about 5€

wandb: https://wandb.ai/stadeltom-com/huggingface/runs/thxte3cl

Achieves 93% f1, 92 % precision, and 94% recall

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