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# Mongolian-distilbert-base-multilingual-cased-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1344
- Precision: 0.8878
- Recall: 0.9055
- F1: 0.8966
- Accuracy: 0.9739

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2155        | 1.0   | 477  | 0.1297          | 0.8050    | 0.8476 | 0.8257 | 0.9584   |
| 0.1037        | 2.0   | 954  | 0.0951          | 0.8505    | 0.8882 | 0.8690 | 0.9699   |
| 0.0687        | 3.0   | 1431 | 0.0978          | 0.8686    | 0.8924 | 0.8803 | 0.9711   |
| 0.05          | 4.0   | 1908 | 0.1087          | 0.8764    | 0.8955 | 0.8858 | 0.9719   |
| 0.0343        | 5.0   | 2385 | 0.1109          | 0.8781    | 0.8992 | 0.8885 | 0.9729   |
| 0.0264        | 6.0   | 2862 | 0.1169          | 0.8798    | 0.9011 | 0.8903 | 0.9729   |
| 0.0182        | 7.0   | 3339 | 0.1221          | 0.8871    | 0.9051 | 0.8960 | 0.9744   |
| 0.0146        | 8.0   | 3816 | 0.1286          | 0.8846    | 0.9036 | 0.8940 | 0.9735   |
| 0.0109        | 9.0   | 4293 | 0.1347          | 0.8880    | 0.9046 | 0.8962 | 0.9737   |
| 0.0095        | 10.0  | 4770 | 0.1344          | 0.8878    | 0.9055 | 0.8966 | 0.9739   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Mongolian-distilbert-base-multilingual-cased-ner", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer mn license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #mn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Mongolian-distilbert-base-multilingual-cased-ner
================================================


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


* Loss: 0.1344
* Precision: 0.8878
* Recall: 0.9055
* F1: 0.8966
* Accuracy: 0.9739


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 2.0.1+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 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #mn #license-apache-2.0 #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 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3