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# bert-base-multilingual-cased-ner-demo

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1333
- Precision: 0.9160
- Recall: 0.9229
- F1: 0.9194
- Accuracy: 0.9779

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1658        | 1.0   | 572  | 0.1113          | 0.8581    | 0.8792 | 0.8685 | 0.9658   |
| 0.0816        | 2.0   | 1144 | 0.0880          | 0.8950    | 0.9096 | 0.9022 | 0.9737   |
| 0.0554        | 3.0   | 1716 | 0.0935          | 0.8941    | 0.9096 | 0.9018 | 0.9741   |
| 0.0394        | 4.0   | 2288 | 0.1069          | 0.9070    | 0.9189 | 0.9129 | 0.9762   |
| 0.0284        | 5.0   | 2860 | 0.1029          | 0.9007    | 0.9184 | 0.9095 | 0.9752   |
| 0.0192        | 6.0   | 3432 | 0.1110          | 0.9102    | 0.9214 | 0.9157 | 0.9764   |
| 0.0139        | 7.0   | 4004 | 0.1156          | 0.9166    | 0.9272 | 0.9218 | 0.9786   |
| 0.0095        | 8.0   | 4576 | 0.1319          | 0.9091    | 0.9174 | 0.9132 | 0.9761   |
| 0.0066        | 9.0   | 5148 | 0.1313          | 0.9132    | 0.9226 | 0.9179 | 0.9781   |
| 0.0053        | 10.0  | 5720 | 0.1333          | 0.9160    | 0.9229 | 0.9194 | 0.9779   |


### Framework versions

- Transformers 4.28.1
- 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": "bert-base-multilingual-cased-ner-demo", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer mn license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #mn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-multilingual-cased-ner-demo
=====================================


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


* Loss: 0.1333
* Precision: 0.9160
* Recall: 0.9229
* F1: 0.9194
* Accuracy: 0.9779


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 #bert #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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3