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from transformers import BertModel, BertForSequenceClassification
from collections import defaultdict
import matplotlib.pyplot as plt
model_name = 'bert-base-uncased'
model = BertModel.from_pretrained(model_name)
cls_model = BertForSequenceClassification.from_pretrained(model_name)
total_params = 0
total_learnable_params = 0
total_embedding_params = 0
total_encoder_params = 0
total_pooler_params = 0
params_dict = defaultdict(float)
for name, para in model.named_parameters():
print(name, para.shape, para.numel())
if para.requires_grad:
total_learnable_params += para.numel()
total_params += para.numel()
if 'embedding' in name:
params_dict['embedding'] += para.numel()
total_embedding_params += para.numel()
if 'encoder' in name:
layer_index = name.split('.')[2]
params_dict['encoder({})'.format(layer_index)] += para.numel()
total_encoder_params += para.numel()
if 'pooler' in name:
params_dict['pooler'] += para.numel()
total_pooler_params += para.numel()
print(total_params)
print(total_learnable_params)
print(params_dict)
print(total_embedding_params)
print(total_encoder_params)
print(total_pooler_params)
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