不幸的是，你现在不能。我不认为这是一个好主意，因为它会使PyTorch笨拙。一个流行的解决方案是使用sklearn将其转换为数字类型。
下面是一个简短的示例：

from sklearn import preprocessing
import torch
labels = ['cat', 'dog', 'mouse', 'elephant', 'pandas']
le = preprocessing.LabelEncoder()
targets = le.fit_transform(labels)
# targets: array([0, 1, 2, 3, 4])
targets = torch.as_tensor(targets)
# targets: tensor([0, 1, 2, 3, 4])

由于您可能需要在真标签和转换标签之间进行转换，因此最好存储变量le。

技巧是首先找出列表中单词的最大长度，然后在第二个循环中用零填充Tensor。注意utf8字符串每个字符占用两个字节。

In[]
import torch
words = ['שלום', 'beautiful', 'world']
max_l = 0
ts_list = []
for w in words:
    ts_list.append(torch.ByteTensor(list(bytes(w, 'utf8'))))
    max_l = max(ts_list[-1].size()[0], max_l)
w_t = torch.zeros((len(ts_list), max_l), dtype=torch.uint8)
for i, ts in enumerate(ts_list):
    w_t[i, 0:ts.size()[0]] = ts
w_t
Out[]
tensor([[215, 169, 215, 156, 215, 149, 215, 157,   0],
        [ 98, 101,  97, 117, 116, 105, 102, 117, 108],
        [119, 111, 114, 108, 100,   0,   0,   0,   0]], dtype=torch.uint8)

如果你不想使用sklearn，另一个解决方案是保留原始列表并创建一个额外的索引列表，你可以用它来引用你的原始值。我特别需要这个，当我必须跟踪我的原始字符串，同时批处理标记化的字符串。
示例如下：

labels = ['cat', 'dog', 'mouse']
sentence_idx = np.linspace(0,len(labels), len(labels), False)
# [0, 1, 2]
torch_idx = torch.tensor(sentence_idx)
# do what ever you would like from torch eg. pass it to a dataloader
dataset = TensorDataset(torch_idx)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in iter(loader):
    print(batch[0])
    print(labels[int(batch[0].item())])
# output:
# tensor([0.], dtype=torch.float64)
# cat
# tensor([1.], dtype=torch.float64)
# dog
# tensor([2.], dtype=torch.float64)
# mouse

对于我的特定用例，代码如下所示：

input_ids, attention_masks, labels = tokenize_sentences(tokenizer, sentences, labels, max_length)
# create a indexes tensor to keep track of original sentence index
sentence_idx = np.linspace(0,len(sentences), len(sentences),False )
torch_idx = torch.tensor(sentence_idx)
dataset = TensorDataset(input_ids, attention_masks, labels, torch_idx)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in loader:
    _, logit = model(batch[0], 
                     token_type_ids=None,
                     attention_mask=batch[1],
                     labels=batch[2])
    pred_flat = np.argmax(logit.detach(), axis=1).flatten()
    print(pred_flat)
    print(batch[2])
    if pred_flat == batch[2]:
        print("\nThe following sentence was predicted correctly:")
            print(sentences[int(batch[3].item())])

truth = [float(truths) for x in truths]
truth = np.asarray(truth)
truth = torch.from_numpy(truth)