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qixun/
bert-chinese-poem

Name: bert-chinese-poem
Availability: InStock

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适用于中国古典诗歌的bert模型，在搜韵开源的语料上以16的batch_size训练了110万步左右，loss稳定低于1。

使用方法如下：

python

from transformers import BertTokenizer, BertForMaskedLM
import torch

# 加载分词器
tokenizer = BertTokenizer.from_pretrained("qixun/bert-chinese-poem")

# 加载模型
model = BertForMaskedLM.from_pretrained("qixun/bert-chinese-poem")

# 输入文本
text = "宵凉百念集孤[MASK]，暗雨鸣廊睡未能。生计坐怜秋一叶，归程冥想浪千层。寒心国事浑难料，堆眼官资信可憎。此去梦中应不忘，顺承门内近觚棱。"

# 分词
inputs = tokenizer(text, return_tensors="pt")

# 模型推理
with torch.no_grad():
    outputs = model(**inputs)

# 获取[MASK]标记的位置
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]

# 获取预测的token_id
predicted_token_id = outputs.logits[0, mask_token_index].argmax(axis=-1).item()

# 获取预测的词
predicted_token = tokenizer.decode([predicted_token_id])

print(f"预测的词是：{predicted_token}")