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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- chn_senti_corp |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: kt_punc |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: chn_senti_corp |
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type: chn_senti_corp |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7078651685393258 |
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- name: Recall |
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type: recall |
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value: 0.7313662547821116 |
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- name: F1 |
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type: f1 |
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value: 0.7194238380517767 |
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- name: Accuracy |
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type: accuracy |
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value: 0.957316742326961 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# kt_punc |
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the chn_senti_corp dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1703 |
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- Precision: 0.7079 |
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- Recall: 0.7314 |
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- F1: 0.7194 |
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- Accuracy: 0.9573 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1661 | 1.0 | 600 | 0.1351 | 0.6566 | 0.6833 | 0.6697 | 0.9498 | |
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| 0.1246 | 2.0 | 1200 | 0.1330 | 0.6854 | 0.6665 | 0.6758 | 0.9521 | |
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| 0.1121 | 3.0 | 1800 | 0.1303 | 0.6885 | 0.6994 | 0.6939 | 0.9537 | |
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| 0.1008 | 4.0 | 2400 | 0.1359 | 0.6836 | 0.7248 | 0.7036 | 0.9543 | |
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| 0.0809 | 5.0 | 3000 | 0.1404 | 0.7035 | 0.7082 | 0.7059 | 0.9559 | |
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| 0.0696 | 6.0 | 3600 | 0.1449 | 0.6986 | 0.7224 | 0.7103 | 0.9560 | |
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| 0.0628 | 7.0 | 4200 | 0.1563 | 0.7063 | 0.7214 | 0.7138 | 0.9567 | |
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| 0.0561 | 8.0 | 4800 | 0.1618 | 0.7024 | 0.7333 | 0.7175 | 0.9568 | |
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| 0.0525 | 9.0 | 5400 | 0.1669 | 0.7083 | 0.7335 | 0.7207 | 0.9574 | |
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| 0.0453 | 10.0 | 6000 | 0.1703 | 0.7079 | 0.7314 | 0.7194 | 0.9573 | |
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### Framework versions |
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- Transformers 4.19.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.1 |
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- Tokenizers 0.12.1 |
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