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# mongolian-xlm-roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1084
- Precision: 0.9271
- Recall: 0.9333
- F1: 0.9302
- Accuracy: 0.9798

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2095        | 1.0   | 477  | 0.0872          | 0.8995    | 0.9140 | 0.9067 | 0.9744   |
| 0.0849        | 2.0   | 954  | 0.0877          | 0.9162    | 0.9247 | 0.9205 | 0.9776   |
| 0.0622        | 3.0   | 1431 | 0.0775          | 0.9239    | 0.9317 | 0.9278 | 0.9791   |
| 0.0453        | 4.0   | 1908 | 0.0841          | 0.9220    | 0.9321 | 0.9270 | 0.9789   |
| 0.0347        | 5.0   | 2385 | 0.0861          | 0.9228    | 0.9309 | 0.9268 | 0.9789   |
| 0.026         | 6.0   | 2862 | 0.0949          | 0.9189    | 0.9305 | 0.9247 | 0.9781   |
| 0.0207        | 7.0   | 3339 | 0.0992          | 0.9290    | 0.9328 | 0.9309 | 0.9798   |
| 0.0157        | 8.0   | 3816 | 0.1043          | 0.9264    | 0.9346 | 0.9305 | 0.9801   |
| 0.0119        | 9.0   | 4293 | 0.1058          | 0.9278    | 0.9327 | 0.9303 | 0.9799   |
| 0.0096        | 10.0  | 4770 | 0.1084          | 0.9271    | 0.9333 | 0.9302 | 0.9798   |


### Framework versions

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

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

mongolian-xlm-roberta-base
==========================


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


* Loss: 0.1084
* Precision: 0.9271
* Recall: 0.9333
* F1: 0.9302
* Accuracy: 0.9798


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 #xlm-roberta #token-classification #generated_from_trainer #mn #license-mit #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