chinese-crypto-sentiment

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 加载模型和分词器 | Load model and tokenizer
model_name = "LocalOptimum/chinese-crypto-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 分析文本 | Analyze text
text = "比特币突破10万美元创历史新高"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# 预测 | Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

# 结果映射 | Result mapping
labels = ['positive', 'neutral', 'negative']
sentiment = labels[predicted_class]
confidence = predictions[0][predicted_class].item()

print(f"情感: {sentiment}")
print(f"置信度: {confidence:.4f}")

texts = [
    "币安获得阿布扎比监管授权",
    "以太坊完成Fusaka升级",
    "某交易所遭攻击损失100万美元"
]

inputs = tokenizer(texts, return_tensors="pt", truncation=True,
                   max_length=128, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_classes = torch.argmax(predictions, dim=-1)

labels = ['positive', 'neutral', 'negative']
for text, pred in zip(texts, predicted_classes):
    print(f"{text} -> {labels[pred]}")