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# mongolian-facebook-xlm-v-base-ner

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1036
- Precision: 0.9263
- Recall: 0.9352
- F1: 0.9307
- Accuracy: 0.9792

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3409        | 1.0   | 477  | 0.1186          | 0.8832    | 0.9019 | 0.8924 | 0.9691   |
| 0.0953        | 2.0   | 954  | 0.0883          | 0.9130    | 0.9235 | 0.9182 | 0.9770   |
| 0.066         | 3.0   | 1431 | 0.0837          | 0.9166    | 0.9264 | 0.9215 | 0.9768   |
| 0.0487        | 4.0   | 1908 | 0.0918          | 0.9244    | 0.9286 | 0.9265 | 0.9778   |
| 0.0388        | 5.0   | 2385 | 0.0902          | 0.9218    | 0.9317 | 0.9268 | 0.9787   |
| 0.0304        | 6.0   | 2862 | 0.0955          | 0.9202    | 0.9296 | 0.9249 | 0.9780   |
| 0.0226        | 7.0   | 3339 | 0.0992          | 0.9226    | 0.9311 | 0.9269 | 0.9781   |
| 0.0192        | 8.0   | 3816 | 0.0962          | 0.9256    | 0.9328 | 0.9292 | 0.9790   |
| 0.0153        | 9.0   | 4293 | 0.1025          | 0.9243    | 0.9347 | 0.9295 | 0.9791   |
| 0.0133        | 10.0  | 4770 | 0.1036          | 0.9263    | 0.9352 | 0.9307 | 0.9792   |


### 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-facebook-xlm-v-base-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-facebook-xlm-v-base-ner
=================================


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


* Loss: 0.1036
* Precision: 0.9263
* Recall: 0.9352
* F1: 0.9307
* Accuracy: 0.9792


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