本文介绍了在哪里可以为BERT获得预训练的词embeddinngs?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我知道BERT的总词汇量为30522，其中包含一些单词和子单词.我想获取BERT的初始输入嵌入.因此，我的要求是获取尺寸为 [30522，768] 的表，我可以通过令牌ID对其进行索引以获取其嵌入.我在哪里可以找到这张桌子?
I know that BERT has total vocabulary size of 30522 which contains some words and subwords. I want to get the initial input embeddings of BERT. So, my requirement is to get the table of size [30522, 768] to which I can index by token id to get its embeddings. Where can I get this table?
推荐答案
BertModel具有 get_input_embeddings():
The BertModels have get_input_embeddings():
import torch
from transformers import BertModel, BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert = BertModel.from_pretrained('bert-base-uncased')
token_embedding = {token: bert.get_input_embeddings()(torch.tensor(id)) for token, id in tokenizer.get_vocab().items()}
print(len(token_embedding))
print(token_embedding['[CLS]'])
输出:
30522
tensor([ 1.3630e-02, -2.6490e-02, -2.3503e-02, -7.7876e-03, 8.5892e-03,
-7.6645e-03, -9.8808e-03, 6.0184e-03, 4.6921e-03, -3.0984e-02,
1.8883e-02, -6.0093e-03, -1.6652e-02, 1.1684e-02, -3.6245e-02,
8.3482e-03, -1.2112e-03, 1.0322e-02, 1.6692e-02, -3.0354e-02,
-1.2372e-02, -2.5173e-02, -8.9602e-03, 8.1994e-03, -2.0011e-02,
-1.5901e-02, -3.8394e-03, 1.4241e-03, 7.0500e-03, 1.6092e-03,
-2.7764e-03, 9.4931e-03, -2.2768e-02, 1.9317e-02, -1.3442e-02,
-2.3763e-02, -1.4617e-02, 9.7735e-03, -2.2428e-03, 3.0642e-02,
6.7829e-03, -2.6471e-03, -1.8553e-02, -1.2363e-02, 7.6489e-03,
-2.5461e-03, -3.1498e-01, 6.3761e-03, 4.8914e-02, -7.7636e-03,
6.0919e-02, 2.1346e-02, -3.9741e-02, 2.2853e-01, 2.6502e-02,
-1.0144e-03, -7.8480e-03, -1.9995e-03, 1.7057e-02, -3.3270e-02,
4.5421e-03, 6.1751e-03, -1.0077e-01, -2.0973e-02, -1.4512e-04,
-9.6657e-03, 1.0871e-02, -1.4786e-02, 2.6437e-04, 2.1166e-02,
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-2.5566e-02, 5.2494e-03, 1.5890e-02, -5.1504e-03, -7.5859e-03,
2.0259e-02, -7.0155e-03, 1.6359e-02, 1.7487e-02, 5.4297e-03,
-8.6403e-03, 2.8821e-02, -7.8964e-03, 1.9259e-02, 2.3868e-02,
-4.3472e-03, 5.5662e-02, -2.1940e-02, 4.1779e-03, -5.7216e-03,
2.6712e-02, -5.0371e-03, 2.4923e-02, -1.3429e-02, -8.4337e-03,
9.8188e-02, -1.2940e-03, 1.2865e-02, -1.5930e-03, 3.6437e-03,
1.5569e-02, 1.8620e-02, -9.0643e-03, -1.9740e-02, 1.0530e-02,
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1.0016e-02, -1.0955e-02, 4.5133e-03, -5.1150e-03, 4.9968e-03,
1.7852e-02, 1.1313e-02, 2.6519e-03, 3.3658e-01, -1.8168e-02,
1.3170e-02, 7.3927e-03, 5.2521e-03, -9.6230e-03, 1.2844e-02,
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1.4717e-02, 4.4342e-03, 1.6012e-02, -1.0427e-02, -2.5767e-02,
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2.1353e-02, 2.3703e-02, -2.0252e-02, 2.1580e-02, 7.2652e-03,
2.0933e-01, 1.2108e-02, 1.0869e-02, 7.0568e-03, -3.1132e-02,
2.0505e-02, 3.2248e-03, -2.2724e-03, 5.5342e-03, 3.0563e-03,
1.9542e-02, 1.2827e-03, 1.5952e-02, -1.5458e-02, -3.8455e-03,
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2.0000e-02, 2.0485e-02, 1.0977e-02, 1.7720e-02, 1.3532e-02,
7.3682e-03, 3.4906e-04, 1.8772e-03, 1.9976e-02, -3.2041e-02,
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-1.1482e-02, 8.3907e-03, -6.4162e-03, 1.5816e-02, 7.8921e-03,
4.4177e-03, 2.2568e-02, 1.0239e-02, -3.0194e-04, 1.3294e-02,
-2.1606e-02, 3.8832e-03, 2.4475e-02, 4.3808e-02, -2.1031e-03,
-1.2163e-02, -4.0786e-02, 1.5565e-02, 1.4750e-02, 1.6645e-02,
2.8083e-02, 1.8920e-03, -1.4733e-04, -2.6208e-02, 2.3780e-02,
1.8657e-04, -2.2931e-03, 3.0334e-03, -1.7294e-02, -2.3001e-02,
8.6004e-03, -3.3497e-02, 2.5660e-02, -1.9225e-02, -2.7186e-02,
-2.1020e-02, -3.5213e-02, -1.8228e-03, -8.2840e-03, 1.1212e-02,
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7.4498e-03, 5.7142e-03, 2.8401e-02, -7.7551e-03, 1.0682e-02,
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8.0935e-03, 3.1139e-03, -5.5047e-03, -3.1347e-02, 2.2140e-02,
1.0865e-02, -2.7849e-02, -4.9580e-03, 1.8804e-03, 1.0007e-01,
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-1.3871e-02, 1.1439e-02, -9.0208e-03, 1.2580e-02, -2.5973e-02,
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4.2518e-03, 1.8941e-02, -6.3909e-03, 8.0590e-03, -6.7893e-03,
6.0878e-03, -5.3970e-03, 7.5776e-04, 1.1374e-03, -5.0035e-03,
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-1.1465e-01, 2.7017e-03, -4.4961e-03, 2.9762e-04, -3.9612e-03,
8.9038e-05, 2.8683e-02, 5.0068e-03, 1.6509e-02, 7.8983e-04,
5.7728e-03, 3.2685e-02, -1.0457e-01, 1.2989e-02, 1.1278e-02,
1.1943e-02, 1.5258e-02, -6.2411e-04, 1.0682e-04, 1.2087e-02,
7.2984e-03, 2.7758e-02, 1.7572e-02, -6.0345e-03, 1.7211e-02,
1.4121e-02, 6.4663e-02, 9.1813e-03, 3.2555e-03, -3.2667e-02,
2.9132e-02, -1.7770e-02, 1.5302e-03, -2.9944e-02, -2.0706e-02,
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6.9636e-03, -8.0838e-03, -2.4583e-03, -2.0677e-02, 8.8132e-03,
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-6.8554e-03, 2.7829e-01, 2.4060e-02, 1.1130e-02, 7.6095e-04,
3.1341e-01, 2.1668e-02, 1.0277e-02, -3.0065e-02, -8.3565e-03,
5.2488e-03, -1.1287e-02, -1.8266e-02, 1.1814e-02, 1.2662e-02,
2.9036e-04, 7.0254e-04, -1.4084e-02, 1.2925e-02, 3.9504e-03,
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3.0703e-02, -4.9538e-03, -6.1673e-02, -6.4986e-03, -2.1317e-02,
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1.5160e-02, 1.8882e-02, 5.1557e-03, -8.8616e-03, 4.2153e-03,
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2.1052e-03, 5.5644e-03, 1.3607e-03, 8.8388e-03, 1.0244e-02,
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3.0928e-02, -8.9599e-03, -1.1446e-02, -1.3794e-02, 7.1943e-03,
-5.8965e-03, 2.2605e-03, -2.6114e-02, -5.6616e-03, 6.5073e-03,
9.2219e-02, -6.7243e-03, 4.4427e-04, 7.2846e-03, -1.1021e-02,
7.8802e-04, -3.8878e-03, 1.0489e-02, 9.2883e-03, 1.8895e-02,
2.1808e-02, 6.2590e-04, -2.6519e-02, 7.0343e-04, -2.9067e-02,
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-1.1450e-02, -8.8985e-04, -4.4062e-03, 2.3629e-02, -2.7221e-02,
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-8.4473e-03, 2.8536e-02, 1.4117e-03, -1.2136e-02, -1.4781e-02,
4.9960e-03, 3.3916e-02, 5.2710e-03, 1.7382e-02, -4.6315e-03,
1.1680e-02, -9.1395e-03, 1.8310e-02, 1.2321e-02, -2.4871e-02,
1.1535e-02, 5.0308e-03, 5.5028e-03, -7.2184e-03, -5.5210e-03,
1.7085e-02, 5.7236e-03, 1.7463e-03, 1.9969e-03, 6.1670e-03,
2.9347e-03, 1.3946e-02, -1.9984e-03, 1.0091e-02, 1.0388e-03,
-6.1902e-03, 3.0905e-02, 6.6038e-03, -9.1223e-02, -1.8411e-02,
5.4185e-03, 2.4396e-02, 1.5696e-02, -1.2742e-02, 1.8126e-02,
-2.6138e-02, 1.1170e-02, -1.3058e-02, -1.9386e-02, -5.9828e-03,
1.9176e-02, 1.9962e-03, -2.1538e-03, 3.3003e-02, 1.8407e-02,
-5.9498e-03, -3.2533e-03, -1.8917e-02, -1.5897e-02, -4.7057e-03,
5.4162e-03, -3.0037e-02, 8.6773e-03, -1.7942e-03, 6.6826e-03,
-1.1929e-02, -1.4076e-02, 1.6709e-02, 1.6860e-03, -3.3842e-03,
8.6805e-03, 7.1340e-03, 1.5147e-02], grad_fn=<EmbeddingBackward>)
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