Create README.md

README.md

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+---
+license: mit
+inference: False
+---
+
+# PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。
+# cluner NER实验
+```python
+# globalpointer
+04/08/2022 10:53:34 - INFO - __main__ - ADDRESS = Score(f1=0.607703, precision=0.64939, recall=0.571046, tp=213, pred=328, gold=373)
+04/08/2022 10:53:34 - INFO - __main__ - BOOK = Score(f1=0.8125, precision=0.873134, recall=0.75974, tp=117, pred=134, gold=154)
+04/08/2022 10:53:34 - INFO - __main__ - COMPANY = Score(f1=0.818304, precision=0.832877, recall=0.804233, tp=304, pred=365, gold=378)
+04/08/2022 10:53:34 - INFO - __main__ - GAME = Score(f1=0.854305, precision=0.834951, recall=0.874576, tp=258, pred=309, gold=295)
+04/08/2022 10:53:34 - INFO - __main__ - GOVERNMENT = Score(f1=0.823529, precision=0.775, recall=0.878543, tp=217, pred=280, gold=247)
+04/08/2022 10:53:34 - INFO - __main__ - MOVIE = Score(f1=0.810997, precision=0.842857, recall=0.781457, tp=118, pred=140, gold=151)
+04/08/2022 10:53:34 - INFO - __main__ - NAME = Score(f1=0.874042, precision=0.890625, recall=0.858065, tp=399, pred=448, gold=465)
+04/08/2022 10:53:34 - INFO - __main__ - ORGANIZATION = Score(f1=0.813986, precision=0.836207, recall=0.792916, tp=291, pred=348, gold=367)
+04/08/2022 10:53:34 - INFO - __main__ - POSITION = Score(f1=0.78478, precision=0.808824, recall=0.762125, tp=330, pred=408, gold=433)
+04/08/2022 10:53:34 - INFO - __main__ - SCENE = Score(f1=0.683805, precision=0.738889, recall=0.636364, tp=133, pred=180, gold=209)
+04/08/2022 10:53:34 - INFO - __main__ - micro_f1 = Score(f1=0.79175, precision=0.809524, recall=0.77474, tp=2380, pred=2940, gold=3072)
+04/08/2022 10:53:34 - INFO - __main__ - macro_f1 = Score(f1=0.788395, precision=0.808275, recall=0.771906, tp=0, pred=0, gold=0)
+04/08/2022 10:53:34 - INFO - __main__ - mean_f1 = 0.790072
+
+# softmax
+04/08/2022 11:10:44 - INFO - __main__ - ADDRESS = Score(f1=0.568987, precision=0.522422, recall=0.624665, tp=233, pred=446, gold=373)
+04/08/2022 11:10:44 - INFO - __main__ - BOOK = Score(f1=0.750789, precision=0.730061, recall=0.772727, tp=119, pred=163, gold=154)
+04/08/2022 11:10:44 - INFO - __main__ - COMPANY = Score(f1=0.75528, precision=0.711944, recall=0.804233, tp=304, pred=427, gold=378)
+04/08/2022 11:10:44 - INFO - __main__ - GAME = Score(f1=0.811502, precision=0.767372, recall=0.861017, tp=254, pred=331, gold=295)
+04/08/2022 11:10:44 - INFO - __main__ - GOVERNMENT = Score(f1=0.738636, precision=0.69395, recall=0.789474, tp=195, pred=281, gold=247)
+04/08/2022 11:10:44 - INFO - __main__ - MOVIE = Score(f1=0.74359, precision=0.720497, recall=0.768212, tp=116, pred=161, gold=151)
+04/08/2022 11:10:44 - INFO - __main__ - NAME = Score(f1=0.831967, precision=0.794521, recall=0.873118, tp=406, pred=511, gold=465)
+04/08/2022 11:10:44 - INFO - __main__ - ORGANIZATION = Score(f1=0.754054, precision=0.747989, recall=0.760218, tp=279, pred=373, gold=367)
+04/08/2022 11:10:44 - INFO - __main__ - POSITION = Score(f1=0.742729, precision=0.720174, recall=0.766744, tp=332, pred=461, gold=433)
+04/08/2022 11:10:44 - INFO - __main__ - SCENE = Score(f1=0.628842, precision=0.621495, recall=0.636364, tp=133, pred=214, gold=209)
+04/08/2022 11:10:44 - INFO - __main__ - micro_f1 = Score(f1=0.736335, precision=0.703979, recall=0.77181, tp=2371, pred=3368, gold=3072)
+04/08/2022 11:10:44 - INFO - __main__ - macro_f1 = Score(f1=0.732638, precision=0.703043, recall=0.765677, tp=0, pred=0, gold=0)
+04/08/2022 11:10:44 - INFO - __main__ - mean_f1 = 0.734486
+```
+
+# install
+- https://github.com/JunnYu/FLASHQuad_pytorch
+
+# usage
+```python
+import torch
+from flash import FLASHForMaskedLM
+from transformers import BertTokenizerFast
+tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_base_wwm_cluecorpussmall")
+model = FLASHForMaskedLM.from_pretrained("junnyu/flash_base_wwm_cluecorpussmall")
+model.eval()
+text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！"
+inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512,  return_token_type_ids=False) #这里必须是512，不然结果可能不对。
+with torch.no_grad():
+    pt_outputs = model(**inputs).logits[0]
+
+pt_outputs_sentence = "pytorch: "
+for i, id in enumerate(tokenizer.encode(text)):
+    if id == tokenizer.mask_token_id:
+        val,idx = pt_outputs[i].softmax(-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)}")
+        pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
+    else:
+        pt_outputs_sentence += "".join(
+            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
+print(pt_outputs_sentence)
+# pytorch: 天气预报说今天的天[气+0.994||天+0.0015||空+0.0014||晴+0.0005||阳+0.0003]很好，那么我[们+0.9563||就+0.0381||也+0.0032||俩+0.0004||来+0.0002]一起去公园玩吧！
+```