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# mn-cased-bert-base-named-entity

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: 0.1382
- Precision: 0.8816
- Recall: 0.9031
- F1: 0.8922
- Accuracy: 0.9729

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2157        | 1.0   | 477  | 0.1347          | 0.7747    | 0.8399 | 0.8060 | 0.9544   |
| 0.103         | 2.0   | 954  | 0.1062          | 0.8510    | 0.8853 | 0.8678 | 0.9677   |
| 0.0673        | 3.0   | 1431 | 0.1033          | 0.8549    | 0.8891 | 0.8717 | 0.9693   |
| 0.048         | 4.0   | 1908 | 0.1110          | 0.8610    | 0.8920 | 0.8762 | 0.9690   |
| 0.0347        | 5.0   | 2385 | 0.1175          | 0.8731    | 0.8967 | 0.8848 | 0.9715   |
| 0.0256        | 6.0   | 2862 | 0.1209          | 0.8741    | 0.9021 | 0.8879 | 0.9728   |
| 0.0183        | 7.0   | 3339 | 0.1219          | 0.8765    | 0.9004 | 0.8883 | 0.9732   |
| 0.0133        | 8.0   | 3816 | 0.1296          | 0.8799    | 0.9036 | 0.8916 | 0.9726   |
| 0.011         | 9.0   | 4293 | 0.1375          | 0.8779    | 0.9031 | 0.8903 | 0.9726   |
| 0.009         | 10.0  | 4770 | 0.1382          | 0.8816    | 0.9031 | 0.8922 | 0.9729   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+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": "mn-cased-bert-base-named-entity", "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 

mn-cased-bert-base-named-entity
===============================


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: 0.1382
* Precision: 0.8816
* Recall: 0.9031
* F1: 0.8922
* Accuracy: 0.9729


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 #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 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3