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  base_model: fnlp/bart-base-chinese
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  tags:
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  - generated_from_trainer
 
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  metrics:
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  - accuracy
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  model-index:
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- - name: bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1
 
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  results: []
 
 
 
 
 
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,10 +24,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1
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- This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2611
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- - Accuracy: 0.5862
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  ## Model description
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@@ -65,4 +75,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.2
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- - Tokenizers 0.13.3
 
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  base_model: fnlp/bart-base-chinese
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  tags:
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  - generated_from_trainer
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+ - finance
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  metrics:
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  - accuracy
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  model-index:
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+ - name: >-
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+ bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1
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  results: []
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+ language:
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+ - zh
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+ widget:
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+ - text: >-
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+ 惠誉下调美国3A主权信用评级次日,经济学家看轻评级下调影响,美国7月ADP新增就业超预期爆表。风险情绪被重创,标普、道指、小盘股齐跌约1%,纳指跌超2%创2月以来最差。
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+ 美国超导跌近29%。美债发行海啸即将来袭,10年期美债收益率一度创九个月新高,两年期美债收益率跌幅显著收窄。美元转涨刷新三周半高位。
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+ 商品普跌。油价跌超2%,美油跌穿80美元整数位。黄金失守1940美元至三周新低。
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+ 中国市场方面,美股时段,中概股指跌4%,理想汽车则再创历史新高,离岸人民币一度跌穿7.21元,最深跌270点至一周低位。沪指收跌近1%,医药、银行疲软,超导概念、地产、券商强势。恒指收跌2.47%,南向资金净流入4.02亿港元。
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1
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+ This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on the dataset of Wallstreetcn Morning News Market Overview with overnight index (000001.SH) movement labels.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.632678747177124
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+ - Accuracy: 0.6551724137931034
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  ## Model description
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.2
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+ - Tokenizers 0.13.3