---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-1

This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9129
- Accuracy: 0.5758

## 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   | 34   | 0.6851          | 0.4848   |
| No log        | 2.0   | 68   | 0.5512          | 0.6364   |
| No log        | 3.0   | 102  | 0.5401          | 0.7576   |
| No log        | 4.0   | 136  | 0.6022          | 0.6364   |
| No log        | 5.0   | 170  | 0.6753          | 0.6667   |
| No log        | 6.0   | 204  | 1.1719          | 0.6061   |
| No log        | 7.0   | 238  | 1.3857          | 0.6364   |
| No log        | 8.0   | 272  | 1.7271          | 0.6364   |
| No log        | 9.0   | 306  | 2.0849          | 0.5455   |
| No log        | 10.0  | 340  | 1.9129          | 0.5758   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3