一个txt文件用bert变词向量后用svm分类

首先，您需要使用BERT模型将文本文件转换为词向量。这可以通过使用一些已经实现好的BERT库来完成，例如Hugging Face的Transformers库。您可以使用以下代码将文本文件转换为BERT词向量：

from transformers import BertModel, BertTokenizer
import torch

# Load BERT model and tokenizer
model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)

# Convert text to BERT input tokens
text = "This is some example text."
tokens = tokenizer.encode(text, add_special_tokens=True)

# Convert tokens to PyTorch tensors
input_ids = torch.tensor([tokens])

# Generate the BERT embeddings
with torch.no_grad():
    embeddings = model(input_ids)[0][0]

接下来，您需要准备用于训练SVM分类器的数据集。您可以从文本文件中读取文本和标签，然后使用相同的BertTokenizer将文本转换为词向量。以下是一个示例代码：

import numpy as np

# Read text and labels from file
with open('data.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()

texts = []
labels = []
for line in lines:
    text, label = line.strip().split('\t')
    texts.append(text)
    labels.append(int(label))

# Convert text to BERT input tokens
max_len = 128  # Maximum input length for BERT
input_ids = []
attention_masks = []
for text in texts:
    tokens = tokenizer.encode(text, add_special_tokens=True, max_length=max_len)
    padding_length = max_len - len(tokens)
    input_ids.append(tokens + [0] * padding_length)
    attention_masks.append([1] * len(tokens) + [0] * padding_length)

# Convert input tokens to PyTorch tensors
input_ids = torch.tensor(input_ids)
attention_masks = torch.tensor(attention_masks)
labels = torch.tensor(labels)

# Generate the BERT embeddings
with torch.no_grad():
    embeddings = model(input_ids, attention_masks)[0][:,0,:].numpy()

# Split data into training and testing sets
split_ratio = 0.8
split_index = int(len(embeddings) * split_ratio)
train_embeddings, test_embeddings = embeddings[:split_index], embeddings[split_index:]
train_labels, test_labels = labels[:split_index], labels[split_index:]

最后，您可以使用sklearn库中的SVM分类器进行训练和测试。以下是一个示例代码：

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Train the SVM classifier
clf = SVC(kernel='linear')
clf.fit(train_embeddings, train_labels)

# Test the SVM classifier
pred_labels = clf.predict(test_embeddings)
accuracy = accuracy_score(test_labels, pred_labels)
print('Accuracy:', accuracy)

请注意，上面的示例代码仅用于说明如何使用BERT和SVM进行文本分类，并且可能需要根据您的具体情况进行修改。