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# mn-roberta-base-demo-named-entity

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: 0.1354
- Precision: 0.9239
- Recall: 0.9322
- F1: 0.9280
- Accuracy: 0.9797

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1651        | 1.0   | 477  | 0.0835          | 0.8900    | 0.9145 | 0.9021 | 0.9745   |
| 0.0535        | 2.0   | 954  | 0.0780          | 0.9047    | 0.9243 | 0.9144 | 0.9775   |
| 0.0267        | 3.0   | 1431 | 0.0836          | 0.9184    | 0.9307 | 0.9245 | 0.9790   |
| 0.0159        | 4.0   | 1908 | 0.0936          | 0.9224    | 0.9329 | 0.9276 | 0.9803   |
| 0.0083        | 5.0   | 2385 | 0.1155          | 0.9224    | 0.9307 | 0.9265 | 0.9790   |
| 0.0055        | 6.0   | 2862 | 0.1211          | 0.9222    | 0.9316 | 0.9268 | 0.9793   |
| 0.0034        | 7.0   | 3339 | 0.1258          | 0.9199    | 0.9329 | 0.9263 | 0.9789   |
| 0.0025        | 8.0   | 3816 | 0.1300          | 0.9249    | 0.9339 | 0.9294 | 0.9799   |
| 0.002         | 9.0   | 4293 | 0.1352          | 0.9231    | 0.9313 | 0.9272 | 0.9795   |
| 0.0018        | 10.0  | 4770 | 0.1354          | 0.9239    | 0.9322 | 0.9280 | 0.9797   |


### Framework versions

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

{"language": ["mn"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mn-roberta-base-demo-named-entity", "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 

mn-roberta-base-demo-named-entity
=================================


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: 0.1354
* Precision: 0.9239
* Recall: 0.9322
* F1: 0.9280
* Accuracy: 0.9797


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 #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 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3