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# mongolian-xlm-roberta-large-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1256
- Precision: 0.9361
- Recall: 0.9423
- F1: 0.9392
- Accuracy: 0.9824

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1837        | 1.0   | 477  | 0.0939          | 0.8524    | 0.8895 | 0.8705 | 0.9745   |
| 0.0736        | 2.0   | 954  | 0.0731          | 0.9318    | 0.9370 | 0.9344 | 0.9809   |
| 0.0525        | 3.0   | 1431 | 0.0724          | 0.9244    | 0.9311 | 0.9278 | 0.9795   |
| 0.036         | 4.0   | 1908 | 0.0807          | 0.9312    | 0.9409 | 0.9361 | 0.9819   |
| 0.0248        | 5.0   | 2385 | 0.0855          | 0.9314    | 0.9407 | 0.9360 | 0.9814   |
| 0.0163        | 6.0   | 2862 | 0.1014          | 0.9327    | 0.9397 | 0.9362 | 0.9815   |
| 0.0112        | 7.0   | 3339 | 0.0997          | 0.9354    | 0.9433 | 0.9393 | 0.9822   |
| 0.0064        | 8.0   | 3816 | 0.1171          | 0.9384    | 0.9432 | 0.9408 | 0.9824   |
| 0.0049        | 9.0   | 4293 | 0.1237          | 0.9355    | 0.9418 | 0.9387 | 0.9822   |
| 0.0024        | 10.0  | 4770 | 0.1256          | 0.9361    | 0.9423 | 0.9392 | 0.9824   |


### 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-large-ner", "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-large-ner
===============================


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


* Loss: 0.1256
* Precision: 0.9361
* Recall: 0.9423
* F1: 0.9392
* Accuracy: 0.9824


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