def evaluate_qa_retriever()

in longform-qa/lfqa_utils.py [0:0]

• 15 lines of code
• 2 McCabe index (conditional complexity)

def evaluate_qa_retriever(model, dataset, tokenizer, args):
    model.eval()
    # make iterator
    eval_sampler = SequentialSampler(dataset)
    model_collate_fn = functools.partial(
        make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
    )
    data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn)
    epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
    tot_loss = 0.0
    with torch.no_grad():
        for step, batch in enumerate(epoch_iterator):
            q_ids, q_mask, a_ids, a_mask = batch
            loss = model(q_ids, q_mask, a_ids, a_mask)
            tot_loss += loss.item()
        return tot_loss / (step + 1)