之前分别用numpy实现了mlp，cnn，lstm，这次搞一个大一点的模型bert，纯numpy实现，最重要的是可在树莓派上或其他不能安装pytorch的板子上运行，推理数据
本次模型是随便在hugging face上找的一个新闻评论的模型，7分类
看这些模型参数，这并不重要，模型占硬盘空间都要400+M
bert.embeddings.word_embeddings.weight torch.Size([21128, 768])
bert.embeddings.position_embeddings.weight torch.Size([512, 768])
bert.embeddings.token_type_embeddings.weight torch.Size([2, 768])
bert.embeddings.LayerNorm.weight torch.Size([768])
bert.embeddings.LayerNorm.bias torch.Size([768])
bert.encoder.layer.0.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.query.bias torch.Size([768])
bert.encoder.layer.0.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.key.bias torch.Size([768])
bert.encoder.layer.0.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.value.bias torch.Size([768])
bert.encoder.layer.0.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.0.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.0.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.0.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.0.output.dense.bias torch.Size([768])
bert.encoder.layer.0.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.0.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.1.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.query.bias torch.Size([768])
bert.encoder.layer.1.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.key.bias torch.Size([768])
bert.encoder.layer.1.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.value.bias torch.Size([768])
bert.encoder.layer.1.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.1.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.1.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.1.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.1.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.1.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.1.output.dense.bias torch.Size([768])
bert.encoder.layer.1.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.1.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.2.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.query.bias torch.Size([768])
bert.encoder.layer.2.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.key.bias torch.Size([768])
bert.encoder.layer.2.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.value.bias torch.Size([768])
bert.encoder.layer.2.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.2.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.2.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.2.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.2.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.2.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.2.output.dense.bias torch.Size([768])
bert.encoder.layer.2.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.2.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.3.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.query.bias torch.Size([768])
bert.encoder.layer.3.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.key.bias torch.Size([768])
bert.encoder.layer.3.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.value.bias torch.Size([768])
bert.encoder.layer.3.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.3.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.3.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.3.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.3.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.3.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.3.output.dense.bias torch.Size([768])
bert.encoder.layer.3.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.3.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.4.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.query.bias torch.Size([768])
bert.encoder.layer.4.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.key.bias torch.Size([768])
bert.encoder.layer.4.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.value.bias torch.Size([768])
bert.encoder.layer.4.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.4.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.4.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.4.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.4.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.4.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.4.output.dense.bias torch.Size([768])
bert.encoder.layer.4.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.4.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.5.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.query.bias torch.Size([768])
bert.encoder.layer.5.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.key.bias torch.Size([768])
bert.encoder.layer.5.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.value.bias torch.Size([768])
bert.encoder.layer.5.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.5.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.5.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.5.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.5.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.5.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.5.output.dense.bias torch.Size([768])
bert.encoder.layer.5.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.5.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.6.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.query.bias torch.Size([768])
bert.encoder.layer.6.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.key.bias torch.Size([768])
bert.encoder.layer.6.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.value.bias torch.Size([768])
bert.encoder.layer.6.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.6.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.6.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.6.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.6.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.6.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.6.output.dense.bias torch.Size([768])
bert.encoder.layer.6.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.6.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.7.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.query.bias torch.Size([768])
bert.encoder.layer.7.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.key.bias torch.Size([768])
bert.encoder.layer.7.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.value.bias torch.Size([768])
bert.encoder.layer.7.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.7.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.7.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.7.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.7.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.7.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.7.output.dense.bias torch.Size([768])
bert.encoder.layer.7.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.7.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.8.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.query.bias torch.Size([768])
bert.encoder.layer.8.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.key.bias torch.Size([768])
bert.encoder.layer.8.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.value.bias torch.Size([768])
bert.encoder.layer.8.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.8.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.8.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.8.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.8.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.8.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.8.output.dense.bias torch.Size([768])
bert.encoder.layer.8.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.8.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.9.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.query.bias torch.Size([768])
bert.encoder.layer.9.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.key.bias torch.Size([768])
bert.encoder.layer.9.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.value.bias torch.Size([768])
bert.encoder.layer.9.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.9.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.9.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.9.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.9.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.9.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.9.output.dense.bias torch.Size([768])
bert.encoder.layer.9.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.9.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.10.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.query.bias torch.Size([768])
bert.encoder.layer.10.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.key.bias torch.Size([768])
bert.encoder.layer.10.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.value.bias torch.Size([768])
bert.encoder.layer.10.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.10.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.10.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.10.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.10.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.10.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.10.output.dense.bias torch.Size([768])
bert.encoder.layer.10.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.10.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.11.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.query.bias torch.Size([768])
bert.encoder.layer.11.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.key.bias torch.Size([768])
bert.encoder.layer.11.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.value.bias torch.Size([768])
bert.encoder.layer.11.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.11.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.11.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.11.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.11.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.11.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.11.output.dense.bias torch.Size([768])
bert.encoder.layer.11.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.11.output.LayerNorm.bias torch.Size([768])
bert.pooler.dense.weight torch.Size([768, 768])
bert.pooler.dense.bias torch.Size([768])
classifier.weight torch.Size([7, 768])
classifier.bias torch.Size([7])
为了实现numpy的bert模型，踩了两天的坑，一步步对比huggingface源码实现的，真的太难了～～～
这是使用numpy实现的bert代码，分数上和huggingface有稍微的一点点区别，可能是模型太大，保存的模型参数误差累计造成的！
看下面的代码真的有利于直接了解bert模型结构，各种细节简单又到位，自己都服自己，研究这个东西～～～
import numpy as np
def word_embedding(input_ids, word_embeddings):
return word_embeddings[input_ids]
def position_embedding(position_ids, position_embeddings):
return position_embeddings[position_ids]
def token_type_embedding(token_type_ids, token_type_embeddings):
return token_type_embeddings[token_type_ids]
def softmax(x, axis=None):
# e_x = np.exp(x).astype(np.float32) #
e_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
sum_ex = np.sum(e_x, axis=axis,keepdims=True).astype(np.float32)
return e_x / sum_ex
def scaled_dot_product_attention(Q, K, V, mask=None):
d_k = Q.shape[-1]
scores = np.matmul(Q, K.transpose(0, 2, 1)) / np.sqrt(d_k)
if mask is not None:
scores = np.where(mask, scores, np.full_like(scores, -np.inf))
attention_weights = softmax(scores, axis=-1)
# print(attention_weights)
# print(np.sum(attention_weights,axis=-1))
output = np.matmul(attention_weights, V)
return output, attention_weights
def multihead_attention(input, num_heads,W_Q,B_Q,W_K,B_K,W_V,B_V,W_O,B_O):
q = np.matmul(input, W_Q.T)+B_Q
k = np.matmul(input, W_K.T)+B_K
v = np.matmul(input, W_V.T)+B_V
# 分割输入为多个头
q = np.split(q, num_heads, axis=-1)
k = np.split(k, num_heads, axis=-1)
v = np.split(v, num_heads, axis=-1)
outputs = []
for q_,k_,v_ in zip(q,k,v):
output, attention_weights = scaled_dot_product_attention(q_, k_, v_)
outputs.append(output)
outputs = np.concatenate(outputs, axis=-1)
outputs = np.matmul(outputs, W_O.T)+B_O
return outputs
def layer_normalization(x, weight, bias, eps=1e-12):
mean = np.mean(x, axis=-1, keepdims=True)
variance = np.var(x, axis=-1, keepdims=True)
std = np.sqrt(variance + eps)
normalized_x = (x - mean) / std
output = weight * normalized_x + bias
return output
def feed_forward_layer(inputs, weight, bias, activation='relu'):
linear_output = np.matmul(inputs,weight) + bias
if activation == 'relu':
activated_output = np.maximum(0, linear_output) # ReLU激活函数
elif activation == 'gelu':
activated_output = 0.5 * linear_output * (1 + np.tanh(np.sqrt(2 / np.pi) * (linear_output + 0.044715 * np.power(linear_output, 3)))) # GELU激活函数
elif activation == "tanh" :
activated_output = np.tanh(linear_output)
else:
activated_output = linear_output # 无激活函数
return activated_output
def residual_connection(inputs, residual):
# 残差连接
residual_output = inputs + residual
return residual_output
def tokenize_sentence(sentence, vocab_file = 'vocab.txt'):
with open(vocab_file, 'r', encoding='utf-8') as f:
vocab = f.readlines()
vocab = [i.strip() for i in vocab]
# print(len(vocab))
tokenized_sentence = ['[CLS]'] + list(sentence) + ["[SEP]"] # 在句子开头添加[cls]
token_ids = [vocab.index(token) for token in tokenized_sentence]
return token_ids
# 加载保存的模型数据
model_data = np.load('bert_model_params.npz')
word_embeddings = model_data["bert.embeddings.word_embeddings.weight"]
position_embeddings = model_data["bert.embeddings.position_embeddings.weight"]
token_type_embeddings = model_data["bert.embeddings.token_type_embeddings.weight"]
def model_input(sentence):
token_ids = tokenize_sentence(sentence)
input_ids = np.array(token_ids) # 输入的词汇id
word_embedded = word_embedding(input_ids, word_embeddings)
position_ids = np.array(range(len(input_ids))) # 位置id
# 位置嵌入矩阵，形状为 (max_position, embedding_size)
position_embedded = position_embedding(position_ids, position_embeddings)
token_type_ids = np.array([0]*len(input_ids)) # 片段类型id
# 片段类型嵌入矩阵，形状为 (num_token_types, embedding_size)
token_type_embedded = token_type_embedding(token_type_ids, token_type_embeddings)
embedding_output = np.expand_dims(word_embedded + position_embedded + token_type_embedded, axis=0)
return embedding_output
def bert(input,num_heads):
ebd_LayerNorm_weight = model_data['bert.embeddings.LayerNorm.weight']
ebd_LayerNorm_bias = model_data['bert.embeddings.LayerNorm.bias']
input = layer_normalization(input,ebd_LayerNorm_weight,ebd_LayerNorm_bias) #这里和模型输出一致
for i in range(12):
# 调用多头自注意力函数
W_Q = model_data['bert.encoder.layer.{}.attention.self.query.weight'.format(i)]
B_Q = model_data['bert.encoder.layer.{}.attention.self.query.bias'.format(i)]
W_K = model_data['bert.encoder.layer.{}.attention.self.key.weight'.format(i)]
B_K = model_data['bert.encoder.layer.{}.attention.self.key.bias'.format(i)]
W_V = model_data['bert.encoder.layer.{}.attention.self.value.weight'.format(i)]
B_V = model_data['bert.encoder.layer.{}.attention.self.value.bias'.format(i)]
W_O = model_data['bert.encoder.layer.{}.attention.output.dense.weight'.format(i)]
B_O = model_data['bert.encoder.layer.{}.attention.output.dense.bias'.format(i)]
attention_output_LayerNorm_weight = model_data['bert.encoder.layer.{}.attention.output.LayerNorm.weight'.format(i)]
attention_output_LayerNorm_bias = model_data['bert.encoder.layer.{}.attention.output.LayerNorm.bias'.format(i)]
intermediate_weight = model_data['bert.encoder.layer.{}.intermediate.dense.weight'.format(i)]
intermediate_bias = model_data['bert.encoder.layer.{}.intermediate.dense.bias'.format(i)]
dense_weight = model_data['bert.encoder.layer.{}.output.dense.weight'.format(i)]
dense_bias = model_data['bert.encoder.layer.{}.output.dense.bias'.format(i)]
output_LayerNorm_weight = model_data['bert.encoder.layer.{}.output.LayerNorm.weight'.format(i)]
output_LayerNorm_bias = model_data['bert.encoder.layer.{}.output.LayerNorm.bias'.format(i)]
output = multihead_attention(input, num_heads,W_Q,B_Q,W_K,B_K,W_V,B_V,W_O,B_O)
output = residual_connection(input,output)
output1 = layer_normalization(output,attention_output_LayerNorm_weight,attention_output_LayerNorm_bias) #这里和模型输出一致
output = feed_forward_layer(output1, intermediate_weight.T, intermediate_bias, activation='gelu')
output = feed_forward_layer(output, dense_weight.T, dense_bias, activation='')
output = residual_connection(output1,output)
output2 = layer_normalization(output,output_LayerNorm_weight,output_LayerNorm_bias) #一致
input = output2
bert_pooler_dense_weight = model_data['bert.pooler.dense.weight']
bert_pooler_dense_bias = model_data['bert.pooler.dense.bias']
output = feed_forward_layer(output2, bert_pooler_dense_weight.T, bert_pooler_dense_bias, activation='tanh') #一致
return output
# for i in model_data:
# # print(i)
# print(i,model_data[i].shape)
id2label = {0: 'mainland China politics', 1: 'Hong Kong - Macau politics', 2: 'International news', 3: 'financial news', 4: 'culture', 5: 'entertainment', 6: 'sports'}
classifier_weight = model_data['classifier.weight']
classifier_bias = model_data['classifier.bias']
if __name__ == "__main__":
sentences = ["马拉松比赛","香港有群众游行示威","党中央决定制定爱国教育法","俄罗斯和欧美对抗","人民币汇率贬值","端午节吃粽子","大妈们跳广场舞"]
while True:
# 示例用法
for sentence in sentences:
# print(model_input(sentence).shape)
output = bert(model_input(sentence),num_heads=12)
# print(output)
output = feed_forward_layer(output[:,0,:], classifier_weight.T, classifier_bias, activation='')
# print(output)
output = softmax(output,axis=-1)
label_id = np.argmax(output,axis=-1)
label_score = output[0][label_id]
print("sentence:",sentence,"\tlabels:",id2label[label_id[0]],"\tscore:",label_score)
这是hugging face上找的一个别人训练好的模型，roberta模型作新闻7分类，并且保存模型结构为numpy格式，为了上面的代码加载
import numpy as np
from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline
model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
print(text_classification("马拉松决赛"))
# print(model)
# 打印BERT模型的权重维度
for name, param in model.named_parameters():
print(name, param.data.shape)
# # # 保存模型参数为NumPy格式
model_params = {name: param.data.cpu().numpy() for name, param in model.named_parameters()}
np.savez('bert_model_params.npz', **model_params)
# model_params
对比两个结果：
hugging face：[{'label': 'sports', 'score': 0.9929242134094238}]
numpy：sports [0.9928773]
多思考也是一种努力，做出正确的分析和选择，因为我们的时间和精力都有限，所以把时间花在更有价值的地方。
