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# mn-xlm-roberta-base-named-entity

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1224
- Precision: 0.9275
- Recall: 0.9364
- F1: 0.9319
- Accuracy: 0.9783

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2015        | 1.0   | 477  | 0.0915          | 0.8830    | 0.9076 | 0.8951 | 0.9724   |
| 0.0837        | 2.0   | 954  | 0.0872          | 0.9089    | 0.9202 | 0.9145 | 0.9757   |
| 0.0605        | 3.0   | 1431 | 0.0814          | 0.9134    | 0.9275 | 0.9204 | 0.9768   |
| 0.0447        | 4.0   | 1908 | 0.0906          | 0.9219    | 0.9316 | 0.9267 | 0.9774   |
| 0.0317        | 5.0   | 2385 | 0.0969          | 0.9229    | 0.9330 | 0.9280 | 0.9782   |
| 0.0254        | 6.0   | 2862 | 0.1121          | 0.9216    | 0.9343 | 0.9279 | 0.9777   |
| 0.0195        | 7.0   | 3339 | 0.1143          | 0.9298    | 0.9364 | 0.9331 | 0.9790   |
| 0.0145        | 8.0   | 3816 | 0.1175          | 0.9229    | 0.9337 | 0.9283 | 0.9773   |
| 0.0114        | 9.0   | 4293 | 0.1205          | 0.9233    | 0.9332 | 0.9282 | 0.9774   |
| 0.0091        | 10.0  | 4770 | 0.1224          | 0.9275    | 0.9364 | 0.9319 | 0.9783   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+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": "mn-xlm-roberta-base-named-entity", "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 

mn-xlm-roberta-base-named-entity
================================


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


* Loss: 0.1224
* Precision: 0.9275
* Recall: 0.9364
* F1: 0.9319
* Accuracy: 0.9783


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 #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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3