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# 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

{"tags": ["generated_from_trainer"], "datasets": ["chn_senti_corp"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "kt_punc", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "chn_senti_corp", "type": "chn_senti_corp", "args": "default"}, "metrics": [{"type": "precision", "value": 0.7078651685393258, "name": "Precision"}, {"type": "recall", "value": 0.7313662547821116, "name": "Recall"}, {"type": "f1", "value": 0.7194238380517767, "name": "F1"}, {"type": "accuracy", "value": 0.957316742326961, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:chn_senti_corp model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-chn_senti_corp #model-index #autotrain_compatible #endpoints_compatible #region-us 

kt\_punc
========


This model is a fine-tuned version of 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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-chn_senti_corp #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-chn_senti_corp #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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