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# bert-finetuned-ner-chinese-people-daily

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the peoples_daily_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.8608
- Recall: 0.8608
- F1: 0.8608
- Accuracy: 0.9853

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 131  | 0.0753          | 0.6955    | 0.7887 | 0.7391 | 0.9764   |
| No log        | 2.0   | 262  | 0.0588          | 0.7971    | 0.8505 | 0.8229 | 0.9840   |
| No log        | 3.0   | 393  | 0.0604          | 0.8608    | 0.8608 | 0.8608 | 0.9853   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["peoples_daily_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner-chinese-people-daily", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "peoples_daily_ner", "type": "peoples_daily_ner", "config": "peoples_daily_ner", "split": "validation", "args": "peoples_daily_ner"}, "metrics": [{"type": "precision", "value": 0.8608247422680413, "name": "Precision"}, {"type": "recall", "value": 0.8608247422680413, "name": "Recall"}, {"type": "f1", "value": 0.8608247422680413, "name": "F1"}, {"type": "accuracy", "value": 0.9852778800147222, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:peoples_daily_ner model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-peoples_daily_ner #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-ner-chinese-people-daily
=======================================


This model is a fine-tuned version of bert-base-chinese on the peoples\_daily\_ner dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0604
* Precision: 0.8608
* Recall: 0.8608
* F1: 0.8608
* Accuracy: 0.9853


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: 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: 3


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-peoples_daily_ner #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3