中文简介
1. 本模型训练于2400条双碳&非双碳话题较短文本，文本取自2010-2021知乎平台。
2. 模型在600条测试集上准确率(accuracy)达98%，F1值为0.9731。

English introduction
1. This model is trained on 2400 short texts on "carbon peaking and carbon neutrality goals" & non "carbon peaking and carbon neutrality goals". The text data is gathered from the 2010-2021 Zhihu platform.
2. The accuracy of the model on the test set (600 pieces) is up to 98%, and the F1 value is approximately 0.9731.
{"license": "mit"}
transformers pytorch bert text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

中文简介
1. 本模型训练于2400条双碳&非双碳话题较短文本，文本取自2010-2021知乎平台。
2. 模型在600条测试集上准确率(accuracy)达98%，F1值为0.9731。

English introduction
1. This model is trained on 2400 short texts on "carbon peaking and carbon neutrality goals" & non "carbon peaking and carbon neutrality goals". The text data is gathered from the 2010-2021 Zhihu platform.
2. The accuracy of the model on the test set (600 pieces) is up to 98%, and the F1 value is approximately 0.9731.
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
