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# roberta-base-ner-demo

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: 0.1349
- Precision: 0.9210
- Recall: 0.9330
- F1: 0.9269
- Accuracy: 0.9788

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1615        | 1.0   | 477  | 0.0917          | 0.8327    | 0.8817 | 0.8565 | 0.9680   |
| 0.0615        | 2.0   | 954  | 0.0917          | 0.8432    | 0.8918 | 0.8668 | 0.9701   |
| 0.0404        | 3.0   | 1431 | 0.0961          | 0.8411    | 0.8970 | 0.8682 | 0.9710   |
| 0.0227        | 4.0   | 1908 | 0.1056          | 0.9081    | 0.9262 | 0.9171 | 0.9770   |
| 0.0133        | 5.0   | 2385 | 0.1117          | 0.8781    | 0.9108 | 0.8942 | 0.9744   |
| 0.0083        | 6.0   | 2862 | 0.1231          | 0.9111    | 0.9288 | 0.9199 | 0.9775   |
| 0.0061        | 7.0   | 3339 | 0.1263          | 0.9110    | 0.9293 | 0.9200 | 0.9776   |
| 0.0071        | 8.0   | 3816 | 0.1316          | 0.9197    | 0.9302 | 0.9249 | 0.9783   |
| 0.0031        | 9.0   | 4293 | 0.1335          | 0.9228    | 0.9327 | 0.9277 | 0.9790   |
| 0.0021        | 10.0  | 4770 | 0.1349          | 0.9210    | 0.9330 | 0.9269 | 0.9788   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

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

roberta-base-ner-demo
=====================


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: 0.1349
* Precision: 0.9210
* Recall: 0.9330
* F1: 0.9269
* Accuracy: 0.9788


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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3