GISchat-weibo-100k-fine-tuned-bert
This model is a fine-tuned version of bert-base-chinese on weibo-100k dataset.
Github repo: https://github.com/GISChat/Fine-tune-bert
It achieves the following results on the evaluation set:
- Loss: 0.0458
- Accuracy: 0.9867
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.08 | 100 | 0.6573 | 0.606 |
0.647 | 0.16 | 200 | 0.2447 | 0.9507 |
0.647 | 0.24 | 300 | 0.0914 | 0.9807 |
0.1276 | 0.32 | 400 | 0.0609 | 0.9843 |
0.1276 | 0.4 | 500 | 0.0607 | 0.9843 |
0.0921 | 0.48 | 600 | 0.1053 | 0.98 |
0.0921 | 0.56 | 700 | 0.0487 | 0.9853 |
0.0885 | 0.64 | 800 | 0.0523 | 0.9853 |
0.0885 | 0.72 | 900 | 0.0484 | 0.986 |
0.0579 | 0.8 | 1000 | 0.0549 | 0.985 |
0.0579 | 0.88 | 1100 | 0.0495 | 0.9867 |
0.0507 | 0.96 | 1200 | 0.0458 | 0.9867 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for wsqstar/GISchat-weibo-100k-fine-tuned-bert
Base model
google-bert/bert-base-chinese