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# xlm-roberta-base-ner-hrl-ner-finetuning

This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1135
- Precision: 0.9290
- Recall: 0.9367
- F1: 0.9328
- Accuracy: 0.9801

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1534        | 1.0   | 477  | 0.0870          | 0.9001    | 0.9124 | 0.9062 | 0.9740   |
| 0.077         | 2.0   | 954  | 0.0764          | 0.9187    | 0.9321 | 0.9253 | 0.9789   |
| 0.0529        | 3.0   | 1431 | 0.0845          | 0.9178    | 0.9313 | 0.9245 | 0.9791   |
| 0.0377        | 4.0   | 1908 | 0.0805          | 0.9200    | 0.9310 | 0.9255 | 0.9795   |
| 0.0292        | 5.0   | 2385 | 0.0918          | 0.9278    | 0.9346 | 0.9312 | 0.9795   |
| 0.0204        | 6.0   | 2862 | 0.1016          | 0.9222    | 0.9323 | 0.9273 | 0.9790   |
| 0.0167        | 7.0   | 3339 | 0.1066          | 0.9271    | 0.9327 | 0.9299 | 0.9790   |
| 0.0134        | 8.0   | 3816 | 0.1088          | 0.9253    | 0.9358 | 0.9305 | 0.9797   |
| 0.0101        | 9.0   | 4293 | 0.1134          | 0.9289    | 0.9357 | 0.9323 | 0.9798   |
| 0.0079        | 10.0  | 4770 | 0.1135          | 0.9290    | 0.9367 | 0.9328 | 0.9801   |


### 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": "xlm-roberta-base-ner-hrl-ner-finetuning", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer mn autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-ner-hrl-ner-finetuning
=======================================


This model is a fine-tuned version of Davlan/xlm-roberta-base-ner-hrl on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1135
* Precision: 0.9290
* Recall: 0.9367
* F1: 0.9328
* Accuracy: 0.9801


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 #xlm-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