在自己计算机上训练Bert不大可能,一般直接采用预训练好的模型 推荐uncased——不区分大小写(Bert-base、Bert-large) gpu数量决定num_worker 数据集 求出和input最相近的10个问题,起到检索引擎的效果 效果 当然,这个模型太简单了,效果可能不太好。有很多烂大街的单词我们都还没踢掉呢hhh NOTE: 1.Bert的用法并不是...
{ "mode": "eval", "max_seq_length": 128, "eval_batch_size": 16, "do_lower_case": true, "data_parallel": true, "need_prepro": false, "model_file": "results/save/model_steps_23000.pt", "eval_data_dir": "data/imdb_sup_test.txt", "vocab":"BERT_Base_Uncased/vocab.txt", ...
import torch from transformers import BertTokenizer, BertModel from sklearn.metrics.pairwise import cosine_similarity # 加载预训练的BERT模型和tokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') # 输入查询 query = "What is ...
from sklearn.metrics.pairwise import cosine_similarity # 加载预训练的BERT模型和tokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') # 输入查询 query = "What is artificial intelligence?" # Convert text to vectors using BER...
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=num_labels ) # 准备训练和验证数据集 train_encodings = tokenizer(train_texts, truncation=True, padding=True) val_encodings = tokenizer...
TFBertModel代码示例 import tensorflow as tf from transformers import BertTokenizer, TFBertModel # Instantiate the tokenizer and the model tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained('bert-base-uncased') # For fun, let's Encode some text inp...
Here is an example of the conversion process for a pre-trainedBERT-Base Uncasedmodel: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 exportBERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12python convert_tf_checkpoint_to_pytorch.py \--tf_checkpoint_path $BERT_BASE_DIR/bert_model....
Here is a quick-start example using BertTokenizer, BertModel and BertForMaskedLM class with Google AI's pre-trained Bert base uncased model. See the doc section below for all the details on these classes. First let's prepare a tokenized input with BertTokenizer import torch from pytorch_pret...
模型会更好。有关多语言和中文模型的信息,请参见(https://github.com/google-research/bert/blob/master/multilingual.md)或原始的TensorFlow存储库。 当使用 Uncased 的模型时,请确保将--do_lower_case传递给示例训练脚本(如果使用自己的脚本,则将 do_lower_case=True ...
BERT-Base, Multilingual Cased (New, recommended): 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Multilingual Uncased (Orig, not recommended): 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters