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# distilbert-base-multilingual-cased-mapa_fine-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0282
- Precision: 0.8763
- Recall: 0.9115
- F1: 0.8936
- Accuracy: 0.9957

## 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: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0244        | 1.0   | 1739  | 0.0202          | 0.8083    | 0.9314 | 0.8655 | 0.9941   |
| 0.0154        | 2.0   | 3478  | 0.0173          | 0.8813    | 0.9006 | 0.8908 | 0.9954   |
| 0.0118        | 3.0   | 5217  | 0.0161          | 0.8885    | 0.9131 | 0.9006 | 0.9960   |
| 0.0084        | 4.0   | 6956  | 0.0194          | 0.8485    | 0.9295 | 0.8871 | 0.9953   |
| 0.0069        | 5.0   | 8695  | 0.0219          | 0.8583    | 0.9198 | 0.8880 | 0.9953   |
| 0.0054        | 6.0   | 10434 | 0.0229          | 0.8622    | 0.9160 | 0.8883 | 0.9954   |
| 0.0032        | 7.0   | 12173 | 0.0248          | 0.8817    | 0.8979 | 0.8898 | 0.9956   |
| 0.0023        | 8.0   | 13912 | 0.0265          | 0.8900    | 0.9023 | 0.8961 | 0.9958   |
| 0.0018        | 9.0   | 15651 | 0.0275          | 0.8657    | 0.9137 | 0.8890 | 0.9954   |
| 0.0016        | 10.0  | 17390 | 0.0282          | 0.8763    | 0.9115 | 0.8936 | 0.9957   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["lextreme"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-multilingual-cased-mapa_fine-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "lextreme", "type": "lextreme", "config": "mapa_fine", "split": "test", "args": "mapa_fine"}, "metrics": [{"type": "precision", "value": 0.8763335204941044, "name": "Precision"}, {"type": "recall", "value": 0.9115199299167762, "name": "Recall"}, {"type": "f1", "value": 0.8935804766335075, "name": "F1"}, {"type": "accuracy", "value": 0.9956876979901592, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors distilbert token-classification generated_from_trainer dataset:lextreme license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-multilingual-cased-mapa\_fine-ner
=================================================


This model is a fine-tuned version of distilbert-base-multilingual-cased on the lextreme dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0282
* Precision: 0.8763
* Recall: 0.9115
* F1: 0.8936
* Accuracy: 0.9957


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: 16
* 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #model-index #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: 16
* 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #model-index #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: 16
* 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2