mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-open-SSEC-v2
This model is a fine-tuned version of Langboat/mengzi-bert-base-fin on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1978
- Accuracy: 0.6562
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 | 0.6805 | 0.5625 |
| No log | 2.0 | 68 | 0.6348 | 0.5938 |
| No log | 3.0 | 102 | 0.7097 | 0.6875 |
| No log | 4.0 | 136 | 0.7510 | 0.6562 |
| No log | 5.0 | 170 | 0.7749 | 0.6562 |
| No log | 6.0 | 204 | 0.8022 | 0.6875 |
| No log | 7.0 | 238 | 1.0138 | 0.625 |
| No log | 8.0 | 272 | 1.1351 | 0.625 |
| No log | 9.0 | 306 | 1.1884 | 0.6562 |
| No log | 10.0 | 340 | 1.1978 | 0.6562 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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Model tree for hw2942/mengzi-bert-base-fin-SSEC
Base model
Langboat/mengzi-bert-base-fin