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# mn-twhin-bert-named-entity

This model is a fine-tuned version of [Twitter/twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1591
- Precision: 0.9068
- Recall: 0.9199
- F1: 0.9133
- Accuracy: 0.9728

## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1901        | 1.0   | 477  | 0.1052          | 0.8528    | 0.8872 | 0.8697 | 0.9666   |
| 0.0853        | 2.0   | 954  | 0.1220          | 0.8731    | 0.8963 | 0.8845 | 0.9666   |
| 0.0577        | 3.0   | 1431 | 0.1109          | 0.8889    | 0.9082 | 0.8984 | 0.9696   |
| 0.0396        | 4.0   | 1908 | 0.1172          | 0.9006    | 0.9175 | 0.9090 | 0.9724   |
| 0.0287        | 5.0   | 2385 | 0.1314          | 0.9002    | 0.9169 | 0.9085 | 0.9720   |
| 0.0213        | 6.0   | 2862 | 0.1363          | 0.9051    | 0.9181 | 0.9116 | 0.9720   |
| 0.0158        | 7.0   | 3339 | 0.1437          | 0.9114    | 0.9221 | 0.9167 | 0.9732   |
| 0.011         | 8.0   | 3816 | 0.1517          | 0.9091    | 0.9202 | 0.9146 | 0.9726   |
| 0.0077        | 9.0   | 4293 | 0.1570          | 0.9070    | 0.9199 | 0.9134 | 0.9728   |
| 0.0059        | 10.0  | 4770 | 0.1591          | 0.9068    | 0.9199 | 0.9133 | 0.9728   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mn-twhin-bert-named-entity", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer mn license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #mn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

mn-twhin-bert-named-entity
==========================


This model is a fine-tuned version of Twitter/twhin-bert-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1591
* Precision: 0.9068
* Recall: 0.9199
* F1: 0.9133
* Accuracy: 0.9728


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: 32
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #mn #license-apache-2.0 #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: 32
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3