from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
 
# 加載預(yù)訓(xùn)練模型
model_name = "finiteautomata/bertweet-base-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
# 定義情感分析函數(shù)
def analyze_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return {
        "negative": probs[0][0].item(),
        "neutral": probs[0][1].item(),
        "positive": probs[0][2].item()
    }
 
# 測試情感分析函數(shù)
print(analyze_sentiment("The product works great but delivery was delayed"))

import librosa
from tensorflow.keras import layers
 
class MultimodalEmotionClassifier(layers.Layer):
    def __init__(self):
        super().__init__()
        self.text_encoder = layers.Bidirectional(layers.LSTM(128))
        self.audio_encoder = layers.Conv1D(64, 3, activation='relu')
        self.fusion = layers.Concatenate()
        self.classifier = layers.Dense(7, activation='softmax')
 
    def call(self, inputs):
        text_feat = self.text_encoder(inputs['text'])
        audio_feat = self.audio_encoder(inputs['audio'])
        combined = self.fusion([text_feat, audio_feat])
        return self.classifier(combined)
 
# 使用示例
# text_input = tokenize("I'm really excited about this!")
# audio_input = librosa.feature.mfcc(y=audio_data, sr=22050)
# model = MultimodalEmotionClassifier()
# prediction = model({'text': text_input, 'audio': audio_input})

import pandas as pd
from sklearn.pipeline import Pipeline
from bertopic import BERTopic
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from multiprocessing import Pool
 
# 自定義文本清洗規(guī)則
class CustomTextCleaner:
    def transform(self, texts):
        # 這里省略了具體的文本清洗代碼
        return texts
 
# 加載微調(diào)后的BERT模型
def load_finetuned_bert():
    model_name = "finiteautomata/bertweet-base-sentiment-analysis"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    return model, tokenizer
 
# 情感分析流水線
class SentimentPipeline:
    def __init__(self):
        self.preprocessor = CustomTextCleaner()
        self.sentiment_model, self.tokenizer = load_finetuned_bert()
        self.topic_model = BERTopic(language="multilingual")
 
    def analyze_batch(self, texts):
        cleaned = self.preprocessor.transform(texts)
        inputs = self.tokenizer(cleaned, return_tensors="pt", truncation=True, max_length=128, padding=True)
        with torch.no_grad():
            outputs = self.sentiment_model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        sentiments = probs.argmax(dim=-1).tolist()
        topics, _ = self.topic_model.fit_transform(cleaned)
        return pd.DataFrame({
            "text": texts,
            "sentiment": sentiments,
            "topic": topics
        })
 
# 分布式分析器
class DistributedAnalyzer:
    def __init__(self, n_workers=4):
        self.pool = Pool(n_workers)
 
    def parallel_analyze(self, chunks):
        return pd.concat(self.pool.map(SentimentPipeline().analyze_batch, chunks))
 
# 示例使用
texts = ["I love this product!", "The delivery was slow.", ...]  # 這里省略了具體的文本數(shù)據(jù)
analyzer = DistributedAnalyzer(n_workers=4)
chunks = [texts[i:i+100] for i in range(0, len(texts), 100)]  # 將文本數(shù)據(jù)分塊處理
results = analyzer.parallel_analyze(chunks)
print(results.head())