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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: 0.1263
- Precision: 0.9352
- Recall: 0.9416
- F1: 0.9384
- Accuracy: 0.9817

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.161         | 1.0   | 477  | 0.0722          | 0.9132    | 0.9248 | 0.9190 | 0.9786   |
| 0.052         | 2.0   | 954  | 0.0732          | 0.9211    | 0.9353 | 0.9282 | 0.9797   |
| 0.028         | 3.0   | 1431 | 0.0802          | 0.9280    | 0.9354 | 0.9317 | 0.9804   |
| 0.015         | 4.0   | 1908 | 0.0954          | 0.9190    | 0.9324 | 0.9257 | 0.9791   |
| 0.0101        | 5.0   | 2385 | 0.0978          | 0.9312    | 0.9385 | 0.9348 | 0.9809   |
| 0.0055        | 6.0   | 2862 | 0.1072          | 0.9315    | 0.9392 | 0.9353 | 0.9810   |
| 0.0035        | 7.0   | 3339 | 0.1165          | 0.9313    | 0.9392 | 0.9352 | 0.9807   |
| 0.0026        | 8.0   | 3816 | 0.1223          | 0.9338    | 0.9403 | 0.9371 | 0.9812   |
| 0.002         | 9.0   | 4293 | 0.1234          | 0.9341    | 0.9398 | 0.9369 | 0.9813   |
| 0.0009        | 10.0  | 4770 | 0.1263          | 0.9352    | 0.9416 | 0.9384 | 0.9817   |


### Framework versions

- Transformers 4.28.1
- 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": "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: 0.1263
* Precision: 0.9352
* Recall: 0.9416
* F1: 0.9384
* Accuracy: 0.9817


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 4.28.1
* 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 4.28.1
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3