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# mongolian-bert-base-multilingual-cased

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1494
- Precision: 0.9081
- Recall: 0.9177
- F1: 0.9129
- Accuracy: 0.9751

## 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: 9

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1721        | 1.0   | 477  | 0.1096          | 0.8521    | 0.8801 | 0.8659 | 0.9667   |
| 0.085         | 2.0   | 954  | 0.1071          | 0.8782    | 0.9002 | 0.8891 | 0.9704   |
| 0.0572        | 3.0   | 1431 | 0.1015          | 0.8902    | 0.9095 | 0.8998 | 0.9737   |
| 0.0394        | 4.0   | 1908 | 0.1091          | 0.8978    | 0.9119 | 0.9048 | 0.9739   |
| 0.0276        | 5.0   | 2385 | 0.1224          | 0.9055    | 0.9174 | 0.9114 | 0.9744   |
| 0.0187        | 6.0   | 2862 | 0.1324          | 0.9032    | 0.9152 | 0.9092 | 0.9744   |
| 0.0126        | 7.0   | 3339 | 0.1426          | 0.9013    | 0.9152 | 0.9082 | 0.9737   |
| 0.0087        | 8.0   | 3816 | 0.1495          | 0.9040    | 0.9160 | 0.9100 | 0.9748   |
| 0.0064        | 9.0   | 4293 | 0.1494          | 0.9081    | 0.9177 | 0.9129 | 0.9751   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+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": "mongolian-bert-base-multilingual-cased", "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 

mongolian-bert-base-multilingual-cased
======================================


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


* Loss: 0.1494
* Precision: 0.9081
* Recall: 0.9177
* F1: 0.9129
* Accuracy: 0.9751


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: 9


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+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: 9 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+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: 9 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3