README.md

---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
- finance
metrics:
- accuracy
model-index:
- name: >-
    bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1
  results: []
language:
- zh
widget:
  - text: >-
      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。
      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。
      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。
      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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# bart-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1

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.
It achieves the following results on the evaluation set:
- Loss: 0.632678747177124
- Accuracy: 0.6551724137931034

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 75   | 0.6801          | 0.5862   |
| No log        | 2.0   | 150  | 0.6871          | 0.5862   |
| No log        | 3.0   | 225  | 0.6725          | 0.5517   |
| No log        | 4.0   | 300  | 0.6327          | 0.6552   |
| No log        | 5.0   | 375  | 0.7839          | 0.5862   |
| No log        | 6.0   | 450  | 0.9481          | 0.5862   |
| 0.6041        | 7.0   | 525  | 1.4396          | 0.5172   |
| 0.6041        | 8.0   | 600  | 1.8405          | 0.6552   |
| 0.6041        | 9.0   | 675  | 2.1651          | 0.5862   |
| 0.6041        | 10.0  | 750  | 2.2611          | 0.5862   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3