Create README.md

README.md

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+---
+language:
+- zh
+tags:
+- generation
+- poetry
+
+---
+
+# 终于落不了油腻俗套， 来弄这劳什子的藏头诗模型
+> This is a model to generated Chinese poetry with leading characters and certain tune of mood.
+
+> 本模型为了达到两个目的
+* 创作藏头诗 🎸
+* 创作时尽量融入关键词的意境🪁 🌼 ❄️ 🌝
+
+## Inference 通道矫情了一点， 大家参数照抄就是了
+
+```python
+
+tokenizer  = AutoTokenizer.from_pretrained('raynardj/keywords-cangtou-chinese-poetry')
+model  = AutoModel.from_pretrained('raynardj/keywords-cangtou-chinese-poetry')
+
+def inference(lead, keywords = []):
+    """
+    lead: 藏头的语句， 比如一个人的名字， 2，3 或4个字
+    keywords：关键词, 0~12个关键词比较好
+    """
+    leading = f"《{lead}》"
+    text = "-".join(keywords)+leading
+    input_ids = tokenizer(text, return_tensors='pt', ).input_ids[:,:-1]
+    lead_tok = tokenizer(lead, return_tensors='pt',  ).input_ids[0,1:-1]
+
+    with torch.no_grad():
+        pred = model.generate(
+            input_ids,
+            max_length=256,
+            num_beams=5,
+            do_sample=True,
+            repetition_penalty=2.1,
+            top_p=.6,
+            bos_token_id=tokenizer.sep_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.sep_token_id,
+        )[0,1:]
+    
+    # 我们需要将[CLS] 字符， 也就是101, 逐个换回藏头的字符
+    mask = (pred==101)
+    while mask.sum()<len(lead_tok):
+        lead_tok = lead_tok[:mask.sum()]
+    while mask.sum()>len(lead_tok):
+        reversed_lead_tok = lead_tok.flip(0)
+        lead_tok = torch.cat([
+            lead_tok, reversed_lead_tok[:mask.sum()-len(lead_tok)]])
+    pred[mask] = lead_tok
+    # 从 token 编号解码成语句
+    generate = tokenizer.decode(pred, skip_special_tokens=True)
+    # 清理语句
+    generate = generate.replace("》","》\n").replace("。","。\n").replace(" ","")
+    return generate
+```
+
+目前可以生成的语句，大家下了模型，🍒可以自己摘
+```python
+>>> inference("上海",["高楼","虹光","灯红酒绿","华厦"])
+高楼-虹光-灯红酒绿-华厦《上海》
+『二』
+上台星月明如昼。
+海阁珠帘卷画堂。
+```