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--- |
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license: gpl-3.0 |
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pipeline_tag: text-classification |
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tags: |
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- art |
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widget: |
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- text: "牛犊初生敢问天，为官一任史无前。钎锤巧构蓝图景，岩壁砺磨钢铁肩。玉汝于成堪大智，红旗永艳有群贤。十风五雨千秋业，铸就惊天动地篇。" |
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- text: "胎禽消息渺难知，小萼妆容故故迟。城郭渐随寒碧敛，湖山刚与晚阴宜，再来恐或成孤往，此去何由问所之。坐对空亭喧冻雀，可堪暝色向人垂。" |
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- text: "异域风吹残帜斜，呜呼水木不清华。未闻史载分赃制，时见官乘夺路槎。有术掠民腾物价，无能让土息胡笳。两朝竭力推经济，遍地催开血色花。" |
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--- |
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此模型的作用是对输入的简体七言律诗进行风格上的分类，详情见 https://mp.weixin.qq.com/s/P8FVCkI8-anDuLWQIAgs2w |
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使用方法如下： |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import json |
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import torch.nn.functional as F |
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from zhconv import convert |
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import re |
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model_path = "qixun/qilv_classify" |
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# 加载模型和分词器 |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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# 如果GPU可用，将模型移动到GPU |
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#device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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#model.to(device) |
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# 加载标签映射关系，label_mapping.json需要根据本机情况修改 |
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with open("label_mapping.json", "r", encoding="utf-8") as f: |
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label_mapping = json.load(f) |
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def classify_text(text): |
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text = convert(text, 'zh-cn') |
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# 去掉空格和换行 |
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text = text.replace(" ", "").replace("\n", "") |
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# 检查文本长度是否为56个字符 |
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if len(text) != 64: |
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return "请输入一首带标点的七言律诗" |
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unique_characters = set(re.findall(r'[\u4e00-\u9fff]', text)) |
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if len(unique_characters) < 30: |
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return "请输入一首正常的七言律诗" |
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# 准备输入数据 |
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inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt", max_length=512) |
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# 如GPU可用，将输入数据移动到GPU |
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#inputs = {key: value.to(device) for key, value in inputs.items()} |
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# 模型推断 |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# 获取预测结果 |
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logits = outputs.logits |
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# 计算每个类别的概率 |
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probabilities = F.softmax(logits, dim=-1) |
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# 获取概率最高的三个分类及其概率 |
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top_k = 3 |
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top_probs, top_indices = torch.topk(probabilities, top_k, dim=-1) |
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# 将预测结果转换为标签并附上概率 |
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results = [] |
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for j in range(top_k): |
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label = label_mapping[str(top_indices[0][j].item())] |
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prob = top_probs[0][j].item() |
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results.append((label, prob)) |
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# 将结果格式化为字符串 |
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result_str = "文本: {}\n".format(text) |
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for label, prob in results: |
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result_str += "分类: {}, 概率: {:.4f}\n".format(label, prob) |
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return result_str |
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# 示例调用 |
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text = "胎禽消息渺难知，小萼妆容故故迟。城郭渐随寒碧敛，湖山刚与晚阴宜，再来恐或成孤往，此去何由问所之。坐对空亭喧冻雀，可堪暝色向人垂。" |
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result = classify_text(text) |
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print(result) |
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``` |
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也可以直接在huggingface里输入一首加标点为64字符的简体七言律诗进行测试，label_mapping.json内容为： |
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{ |
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"0": "中唐", |
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"1": "乱码", |
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"2": "冲塔", |
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"3": "同光", |
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"4": "复兴", |
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"5": "实验", |
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"6": "晚唐", |
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"7": "江西", |
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"8": "浙", |
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"9": "浣花", |
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"10": "理学", |
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"11": "盛唐", |
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"12": "艳体", |
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"13": "诗界xx", |
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"14": "赣", |
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"15": "闽" |
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} |
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大家自行转换。 |
