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kktoto
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kt_punc

Token Classification
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bert
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kt_punc
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---
tags:
- generated_from_trainer
datasets:
- chn_senti_corp
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: kt_punc
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: chn_senti_corp
      type: chn_senti_corp
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.7078651685393258
    - name: Recall
      type: recall
      value: 0.7313662547821116
    - name: F1
      type: f1
      value: 0.7194238380517767
    - name: Accuracy
      type: accuracy
      value: 0.957316742326961
---

<!-- 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. -->

# kt_punc

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the chn_senti_corp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1703
- Precision: 0.7079
- Recall: 0.7314
- F1: 0.7194
- Accuracy: 0.9573

## 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: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1661        | 1.0   | 600  | 0.1351          | 0.6566    | 0.6833 | 0.6697 | 0.9498   |
| 0.1246        | 2.0   | 1200 | 0.1330          | 0.6854    | 0.6665 | 0.6758 | 0.9521   |
| 0.1121        | 3.0   | 1800 | 0.1303          | 0.6885    | 0.6994 | 0.6939 | 0.9537   |
| 0.1008        | 4.0   | 2400 | 0.1359          | 0.6836    | 0.7248 | 0.7036 | 0.9543   |
| 0.0809        | 5.0   | 3000 | 0.1404          | 0.7035    | 0.7082 | 0.7059 | 0.9559   |
| 0.0696        | 6.0   | 3600 | 0.1449          | 0.6986    | 0.7224 | 0.7103 | 0.9560   |
| 0.0628        | 7.0   | 4200 | 0.1563          | 0.7063    | 0.7214 | 0.7138 | 0.9567   |
| 0.0561        | 8.0   | 4800 | 0.1618          | 0.7024    | 0.7333 | 0.7175 | 0.9568   |
| 0.0525        | 9.0   | 5400 | 0.1669          | 0.7083    | 0.7335 | 0.7207 | 0.9574   |
| 0.0453        | 10.0  | 6000 | 0.1703          | 0.7079    | 0.7314 | 0.7194 | 0.9573   |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1