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# testing_mongolian-roberta_base

This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1244
- Precision: 0.9311
- Recall: 0.9399
- F1: 0.9355
- Accuracy: 0.9821

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1683        | 1.0   | 477  | 0.0805          | 0.8377    | 0.8921 | 0.8640 | 0.9730   |
| 0.0545        | 2.0   | 954  | 0.0739          | 0.9205    | 0.9334 | 0.9269 | 0.9806   |
| 0.0292        | 3.0   | 1431 | 0.0778          | 0.9270    | 0.9354 | 0.9312 | 0.9817   |
| 0.0164        | 4.0   | 1908 | 0.0884          | 0.9290    | 0.9360 | 0.9325 | 0.9820   |
| 0.008         | 5.0   | 2385 | 0.1025          | 0.9247    | 0.9365 | 0.9306 | 0.9811   |
| 0.0057        | 6.0   | 2862 | 0.1093          | 0.9294    | 0.9369 | 0.9331 | 0.9815   |
| 0.0037        | 7.0   | 3339 | 0.1173          | 0.9336    | 0.9412 | 0.9374 | 0.9822   |
| 0.0026        | 8.0   | 3816 | 0.1217          | 0.9281    | 0.9374 | 0.9327 | 0.9817   |
| 0.0016        | 9.0   | 4293 | 0.1225          | 0.9334    | 0.9399 | 0.9366 | 0.9821   |
| 0.0012        | 10.0  | 4770 | 0.1244          | 0.9311    | 0.9399 | 0.9355 | 0.9821   |


### Framework versions

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

{"language": ["mn"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "testing_mongolian-roberta_base", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer mn autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us 

testing\_mongolian-roberta\_base
================================


This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1244
* Precision: 0.9311
* Recall: 0.9399
* F1: 0.9355
* Accuracy: 0.9821


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 2.0.1+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: 10 ### 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 #roberta #token-classification #generated_from_trainer #mn #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 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3