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---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Misinformation-Covid-LowLearningRatebert-base-chinese
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. -->
# Misinformation-Covid-LowLearningRatebert-base-chinese
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5999
- F1: 0.2128
## 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-07
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6765 | 1.0 | 189 | 0.6464 | 0.0 |
| 0.6809 | 2.0 | 378 | 0.6449 | 0.0 |
| 0.6734 | 3.0 | 567 | 0.6651 | 0.0 |
| 0.6827 | 4.0 | 756 | 0.6684 | 0.0 |
| 0.7095 | 5.0 | 945 | 0.6532 | 0.0 |
| 0.7 | 6.0 | 1134 | 0.6646 | 0.0 |
| 0.7192 | 7.0 | 1323 | 0.6497 | 0.0 |
| 0.6877 | 8.0 | 1512 | 0.6446 | 0.0 |
| 0.6831 | 9.0 | 1701 | 0.6305 | 0.0571 |
| 0.6633 | 10.0 | 1890 | 0.6203 | 0.1622 |
| 0.6668 | 11.0 | 2079 | 0.6219 | 0.1622 |
| 0.6482 | 12.0 | 2268 | 0.6242 | 0.1111 |
| 0.6543 | 13.0 | 2457 | 0.6117 | 0.15 |
| 0.6492 | 14.0 | 2646 | 0.6236 | 0.1622 |
| 0.6624 | 15.0 | 2835 | 0.6233 | 0.1622 |
| 0.6525 | 16.0 | 3024 | 0.6134 | 0.15 |
| 0.6466 | 17.0 | 3213 | 0.6118 | 0.1905 |
| 0.6406 | 18.0 | 3402 | 0.6191 | 0.15 |
| 0.6479 | 19.0 | 3591 | 0.6216 | 0.1538 |
| 0.6488 | 20.0 | 3780 | 0.6076 | 0.2128 |
| 0.6352 | 21.0 | 3969 | 0.6062 | 0.2174 |
| 0.6213 | 22.0 | 4158 | 0.6042 | 0.2174 |
| 0.6285 | 23.0 | 4347 | 0.6100 | 0.2326 |
| 0.6298 | 24.0 | 4536 | 0.6076 | 0.2128 |
| 0.6473 | 25.0 | 4725 | 0.6058 | 0.2128 |
| 0.5972 | 26.0 | 4914 | 0.6065 | 0.2222 |
| 0.6118 | 27.0 | 5103 | 0.6001 | 0.25 |
| 0.6116 | 28.0 | 5292 | 0.6059 | 0.2128 |
| 0.6289 | 29.0 | 5481 | 0.5992 | 0.25 |
| 0.5932 | 30.0 | 5670 | 0.6006 | 0.25 |
| 0.6076 | 31.0 | 5859 | 0.6009 | 0.2128 |
| 0.6033 | 32.0 | 6048 | 0.6082 | 0.2128 |
| 0.6235 | 33.0 | 6237 | 0.6023 | 0.2128 |
| 0.6237 | 34.0 | 6426 | 0.6079 | 0.2222 |
| 0.6176 | 35.0 | 6615 | 0.6081 | 0.2222 |
| 0.646 | 36.0 | 6804 | 0.6019 | 0.2128 |
| 0.6233 | 37.0 | 6993 | 0.6020 | 0.2128 |
| 0.6004 | 38.0 | 7182 | 0.6040 | 0.2174 |
| 0.6159 | 39.0 | 7371 | 0.5963 | 0.2449 |
| 0.5747 | 40.0 | 7560 | 0.6011 | 0.2174 |
| 0.6216 | 41.0 | 7749 | 0.5954 | 0.2449 |
| 0.5893 | 42.0 | 7938 | 0.5974 | 0.2083 |
| 0.5887 | 43.0 | 8127 | 0.5993 | 0.2128 |
| 0.5756 | 44.0 | 8316 | 0.5993 | 0.2128 |
| 0.6204 | 45.0 | 8505 | 0.5982 | 0.2083 |
| 0.584 | 46.0 | 8694 | 0.5966 | 0.2449 |
| 0.5809 | 47.0 | 8883 | 0.5989 | 0.2083 |
| 0.5873 | 48.0 | 9072 | 0.6002 | 0.2128 |
| 0.5999 | 49.0 | 9261 | 0.6001 | 0.2128 |
| 0.5888 | 50.0 | 9450 | 0.5999 | 0.2128 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3
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