BERT学习笔记

word2vec

预训练word2vec模型

官方API介绍如下：

class gensim.models.word2vec.Word2Vec(sentences=None, corpus_file=None, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=0.001, seed=1, workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=, iter=5, null_word=0, trim_rule=None, sorted_vocab=1, batch_words=10000, compute_loss=False, callbacks=(), max_final_vocab=None)
　主要参数介绍如下：
　　　　1) sentences：我们要分析的语料，可以是一个列表，或者从文件中遍历读出（word2vec.LineSentence(filename) ）。

2) size：词向量的维度，默认值是100。这个维度的取值一般与我们的语料的大小相关，如果是不大的语料，比如小于100M的文本语料，则使用默认值一般就可以了。如果是超大的语料，建议增大维度。

3) window：即词向量上下文最大距离，window越大，则和某一词较远的词也会产生上下文关系。默认值为5，在实际使用中，可以根据实际的需求来动态调整这个window的大小。

如果是小语料则这个值可以设的更小。对于一般的语料这个值推荐在[5；10]之间。

4) sg：即我们的word2vec两个模型的选择了。如果是0， 则是CBOW模型；是1则是Skip-Gram模型；默认是0即CBOW模型。

5) hs：即我们的word2vec两个解法的选择了。如果是0， 则是Negative Sampling；是1的话并且负采样个数negative大于0， 则是Hierarchical Softmax。默认是0即Negative Sampling。

6) negative：即使用Negative Sampling时负采样的个数，默认是5。推荐在[3,10]之间。这个参数在我们的算法原理篇中标记为neg。

7) cbow_mean：仅用于CBOW在做投影的时候，为0，则算法中的xw为上下文的词向量之和，为1则为上下文的词向量的平均值。在我们的原理篇中，是按照词向量的平均值来描述的。个人比较喜欢用平均值来表示xw,默认值也是1,不推荐修改默认值。

8) min_count：需要计算词向量的最小词频。这个值可以去掉一些很生僻的低频词，默认是5。如果是小语料，可以调低这个值。

9) iter：随机梯度下降法中迭代的最大次数，默认是5。对于大语料，可以增大这个值。

10) alpha：在随机梯度下降法中迭代的初始步长。算法原理篇中标记为η，默认是0.025。

11) min_alpha: 由于算法支持在迭代的过程中逐渐减小步长，min_alpha给出了最小的迭代步。

BERT

（摘自huggingface官方文档）
BertModel
classtransformers.BertModel(config, add_pooling_layer=True)[SOURCE]
The bare Bert Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters
config (BertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the is_decoder argument of the configuration set to True. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and add_cross_attention set to True; an encoder_hidden_states is then expected as an input to the forward pass.
forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[SOURCE]
The BertModel forward method, overrides the call() special method.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using BertTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.call() for details.
What are input IDs?
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) –
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
• 1 for tokens that are not masked,
• 0 for tokens that are masked.
What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
• 0 corresponds to a sentence A token,
• 1 corresponds to a sentence B token.
What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
• head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) –
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
• 1 indicates the head is not masked,
• 0 indicates the head is masked.
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.
• encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
• encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) –
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
• 1 for tokens that are not masked,
• 0 for tokens that are masked.
• past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
• use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
Returns
A BaseModelOutputWithPoolingAndCrossAttentions (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (BertConfig) and inputs.
• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
• pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) – Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
• past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
Return type
BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Example:

from transformers import BertTokenizer, BertModel
import torch

tokenizer = BertTokenizer.from_pretrained(‘bert-base-uncased’)
model = BertModel.from_pretrained(‘bert-base-uncased’)

inputs = tokenizer(“Hello, my dog is cute”, return_tensors=“pt”)
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state