|
--- |
|
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 |
|
|
