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# bert_chinese_mc_base-BioNER-EN-ZH

This model is a fine-tuned version of [StivenLancheros/bert_chinese_mc_base-BioNER-EN](https://huggingface.co/StivenLancheros/bert_chinese_mc_base-BioNER-EN) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3611
- Precision: 0.6967
- Recall: 0.7980
- F1: 0.7439
- Accuracy: 0.9215

## 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: 3e-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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4895        | 1.0   | 680   | 0.6248          | 0.4389    | 0.6486 | 0.5235 | 0.8301   |
| 0.3569        | 2.0   | 1360  | 0.6207          | 0.4931    | 0.7204 | 0.5854 | 0.8481   |
| 0.2778        | 3.0   | 2040  | 0.4876          | 0.5723    | 0.7371 | 0.6443 | 0.8864   |
| 0.2558        | 4.0   | 2720  | 0.4496          | 0.5882    | 0.7446 | 0.6572 | 0.8892   |
| 0.2363        | 5.0   | 3400  | 0.4674          | 0.5845    | 0.7619 | 0.6615 | 0.8892   |
| 0.2129        | 6.0   | 4080  | 0.4311          | 0.6148    | 0.7674 | 0.6827 | 0.9005   |
| 0.2019        | 7.0   | 4760  | 0.3930          | 0.6428    | 0.7710 | 0.7011 | 0.9103   |
| 0.1912        | 8.0   | 5440  | 0.4031          | 0.6438    | 0.7815 | 0.7060 | 0.9095   |
| 0.1741        | 9.0   | 6120  | 0.3914          | 0.6506    | 0.7765 | 0.7080 | 0.9101   |
| 0.1727        | 10.0  | 6800  | 0.3808          | 0.6530    | 0.7814 | 0.7114 | 0.9117   |
| 0.1625        | 11.0  | 7480  | 0.4047          | 0.6545    | 0.7828 | 0.7129 | 0.9106   |
| 0.1546        | 12.0  | 8160  | 0.3803          | 0.6543    | 0.7849 | 0.7137 | 0.9115   |
| 0.1515        | 13.0  | 8840  | 0.3635          | 0.6828    | 0.7979 | 0.7359 | 0.9217   |
| 0.1415        | 14.0  | 9520  | 0.3872          | 0.6718    | 0.7962 | 0.7287 | 0.9160   |
| 0.1425        | 15.0  | 10200 | 0.3699          | 0.6879    | 0.7939 | 0.7371 | 0.9193   |
| 0.1327        | 16.0  | 10880 | 0.3762          | 0.6869    | 0.7977 | 0.7382 | 0.9184   |
| 0.1307        | 17.0  | 11560 | 0.3732          | 0.6822    | 0.8013 | 0.7369 | 0.9181   |
| 0.1309        | 18.0  | 12240 | 0.3629          | 0.6956    | 0.7970 | 0.7428 | 0.9208   |
| 0.1268        | 19.0  | 12920 | 0.3643          | 0.6930    | 0.7990 | 0.7423 | 0.9210   |
| 0.1257        | 20.0  | 13600 | 0.3611          | 0.6967    | 0.7980 | 0.7439 | 0.9215   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert_chinese_mc_base-BioNER-EN-ZH", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

bert\_chinese\_mc\_base-BioNER-EN-ZH
====================================


This model is a fine-tuned version of StivenLancheros/bert\_chinese\_mc\_base-BioNER-EN on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3611
* Precision: 0.6967
* Recall: 0.7980
* F1: 0.7439
* Accuracy: 0.9215


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: 3e-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: 20


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: 20### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2