---
# System prepended metadata

title: Untitled

---

```
涉政  spend time: 157.54228496551514
Predict   0     1   2    3   5   All
label
1        22  1350  42  242  80  1736
All      22  1350  42  242  80  1736

Accuracy: 0.7776497695852534
Error amount: 386 / 1736

dataset amount after removing official messages: 5287
dataset amount after removing duplicate messages: 299
C:\Python38\lib\site-packages\transformers\configuration_xlnet.py:205: FutureWarning: This config doesn't use attention memories, a core feature of XLNet. Consider setting `men_len` to a non-zero value, for example `xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`, for accurate training performance as well as an order of magnitude faster inference. Starting from version 3.5.0, the default parameter will be 1024, following the implementation in https://arxiv.org/abs/1906.08237
  warnings.warn(
辱罵  spend time: 28.961681127548218
Predict   0  1    2  3  4   5  All
label
2        10  3  254  1  9  22  299
All      10  3  254  1  9  22  299

Accuracy: 0.8494983277591973
Error amount: 45 / 299

dataset amount after removing official messages: 1183
dataset amount after removing duplicate messages: 1181
C:\Python38\lib\site-packages\transformers\configuration_xlnet.py:205: FutureWarning: This config doesn't use attention memories, a core feature of XLNet. Consider setting `men_len` to a non-zero value, for example `xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`, for accurate training performance as well as an order of magnitude faster inference. Starting from version 3.5.0, the default parameter will be 1024, following the implementation in https://arxiv.org/abs/1906.08237
  warnings.warn(
違禁  spend time: 111.92304039001465
Predict  0   1   2     3  4   5   All
label
3        6  10  30  1052  2  81  1181
All      6  10  30  1052  2  81  1181

Accuracy: 0.890770533446232
Error amount: 129 / 1181

dataset amount after removing official messages: 415
dataset amount after removing duplicate messages: 415
C:\Python38\lib\site-packages\transformers\configuration_xlnet.py:205: FutureWarning: This config doesn't use attention memories, a core feature of XLNet. Consider setting `men_len` to a non-zero value, for example `xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`, for accurate training performance as well as an order of magnitude faster inference. Starting from version 3.5.0, the default parameter will be 1024, following the implementation in https://arxiv.org/abs/1906.08237
  warnings.warn(
色情  spend time: 46.245901346206665
Predict  0   2   3    4   5  All
label
4        6  27  22  336  24  415
All      6  27  22  336  24  415

Accuracy: 0.8096385542168675
Error amount: 79 / 415

dataset amount after removing official messages: 10000
dataset amount after removing duplicate messages: 5786
C:\Python38\lib\site-packages\transformers\configuration_xlnet.py:205: FutureWarning: This config doesn't use attention memories, a core feature of XLNet. Consider setting `men_len` to a non-zero value, for example `xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`, for accurate training performance as well as an order of magnitude faster inference. Starting from version 3.5.0, the default parameter will be 1024, following the implementation in https://arxiv.org/abs/1906.08237
  warnings.warn(
廣告  spend time: 16.618953704833984
Predict   0  2  4     5   All
label
5        23  3  1  5759  5786
All      23  3  1  5759  5786

Accuracy: 0.9953335637746284
Error amount: 27 / 5786

```
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```
```