f79ecd6c4c0bf965d259937d4900cebe54214f10,pythainlp/ulmfit/__init__.py,,document_vector,#Any#Any#Any#,119

Before Change

    pred, _ = m(t)

    // return concatenation of last, mean and max
    last = pred[-1][-1, :, :].squeeze()
    avg_pool = pred[-1].mean(0)[0].squeeze()
    max_pool = pred[-1].max(0)[0].squeeze()
    res = torch.cat((last, avg_pool, max_pool)).detach().cpu().numpy()

    return res

After Change

    
    s = _tokenizer.tokenizer(text)
    t = torch.tensor(data.vocab.numericalize(s), requires_grad=False).to(device)
    m = learn.model[0].encoder.to(device)
    res = m(t).mean(0).cpu().detach().numpy()
    return(res)

Instances