Create README.md

README.md

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+---
+language: zh
+tags:
+- gau-alpha
+- paddlepaddle
+inference: False
+---
+
+# pytorch 和 paddle代码
+https://github.com/JunnYu/GAU-alpha-pytorch
+
+# bert4keras代码
+https://github.com/ZhuiyiTechnology/GAU-alpha
+
+# Install
+```bash
+进入https://github.com/JunnYu/GAU-alpha-pytorch，
+下载paddle代码gau_alpha_paddle
+```
+
+# Usage
+```python
+import paddle
+from transformers import BertTokenizer as GAUAlphaTokenizer
+from gau_alpha_paddle import GAUAlphaForMaskedLM
+
+text = "今天[MASK]很好，我[MASK]去公园玩。"
+tokenizer = GAUAlphaTokenizer.from_pretrained(
+    "junnyu/chinese_GAU-alpha-char_L-24_H-768"
+)
+pd_model = GAUAlphaForMaskedLM.from_pretrained("chinese_GAU-alpha-char_L-24_H-768")
+pd_model.eval()
+pd_inputs = tokenizer(text)
+pd_inputs = {k: paddle.to_tensor([v]) for k, v in pd_inputs.items()}
+
+with paddle.no_grad():
+    pd_outputs = pd_model(**pd_inputs)[0][0]
+
+pd_outputs_sentence = "paddle: "
+for i, id in enumerate(tokenizer.encode(text)):
+    if id == tokenizer.mask_token_id:
+        val, idx = paddle.nn.functional.softmax(pd_outputs[i], -1).topk(k=5)
+        tokens = tokenizer.convert_ids_to_tokens(idx)
+        new_tokens = []
+        for v, t in zip(val.cpu(), tokens):
+            new_tokens.append(f"{t}+{round(v.item(),4)}")
+        pd_outputs_sentence += "[" + "||".join(new_tokens) + "]"
+    else:
+        pd_outputs_sentence += "".join(
+            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
+        )
+print(pd_outputs_sentence)
+# paddle: 今天[天+0.8657||气+0.0535||阳+0.0165||，+0.0126||晴+0.0111]很好，我[要+0.4619||想+0.4352||又+0.0252||就+0.0157||跑+0.0064]去公园玩。
+```
+
+# Reference
+Bibtex：
+
+```tex
+@techreport{gau-alpha,
+  title={GAU-α: GAU-based Transformers for NLP - ZhuiyiAI},
+  author={Jianlin Su, Shengfeng Pan, Bo Wen, Yunfeng Liu},
+  year={2022},
+  url="https://github.com/ZhuiyiTechnology/GAU-alpha",
+}
+```