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# mongolian-xlm-roberta-base-demo

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: 0.1177
- Precision: 0.9262
- Recall: 0.9332
- F1: 0.9297
- Accuracy: 0.9785

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1979        | 1.0   | 477  | 0.1015          | 0.8713    | 0.8958 | 0.8834 | 0.9692   |
| 0.0839        | 2.0   | 954  | 0.0965          | 0.9050    | 0.9125 | 0.9088 | 0.9743   |
| 0.0604        | 3.0   | 1431 | 0.0844          | 0.9217    | 0.9258 | 0.9237 | 0.9771   |
| 0.0455        | 4.0   | 1908 | 0.0955          | 0.9154    | 0.9283 | 0.9218 | 0.9774   |
| 0.0337        | 5.0   | 2385 | 0.0923          | 0.9228    | 0.9318 | 0.9273 | 0.9787   |
| 0.0254        | 6.0   | 2862 | 0.1055          | 0.9213    | 0.9303 | 0.9258 | 0.9776   |
| 0.02          | 7.0   | 3339 | 0.1075          | 0.9244    | 0.9329 | 0.9286 | 0.9785   |
| 0.0149        | 8.0   | 3816 | 0.1142          | 0.9262    | 0.9329 | 0.9295 | 0.9788   |
| 0.0126        | 9.0   | 4293 | 0.1149          | 0.9219    | 0.9306 | 0.9262 | 0.9780   |
| 0.01          | 10.0  | 4770 | 0.1177          | 0.9262    | 0.9332 | 0.9297 | 0.9785   |


### Framework versions

- Transformers 4.29.2
- 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-demo", "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-demo
===============================


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: 0.1177
* Precision: 0.9262
* Recall: 0.9332
* F1: 0.9297
* Accuracy: 0.9785


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 4.29.2
* 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 4.29.2
* 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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3