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# distilbert-base-multilingual-NER-naamapdam-fine-tuned

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: 0.3852
- Precision: 0.7940
- Recall: 0.8182
- F1: 0.8059
- Accuracy: 0.9124

## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3757        | 0.51  | 1000  | 0.3173          | 0.7666    | 0.7858 | 0.7761 | 0.8984   |
| 0.3062        | 1.03  | 2000  | 0.3020          | 0.7791    | 0.7981 | 0.7885 | 0.9026   |
| 0.2793        | 1.54  | 3000  | 0.2962          | 0.7827    | 0.8021 | 0.7923 | 0.9059   |
| 0.2755        | 2.06  | 4000  | 0.2973          | 0.7768    | 0.8122 | 0.7941 | 0.9048   |
| 0.2529        | 2.57  | 5000  | 0.2879          | 0.7747    | 0.8201 | 0.7968 | 0.9057   |
| 0.2483        | 3.08  | 6000  | 0.3025          | 0.7714    | 0.8298 | 0.7996 | 0.9079   |
| 0.2294        | 3.6   | 7000  | 0.2899          | 0.7877    | 0.8211 | 0.8041 | 0.9105   |
| 0.2252        | 4.11  | 8000  | 0.2952          | 0.7850    | 0.8185 | 0.8014 | 0.9090   |
| 0.2088        | 4.63  | 9000  | 0.2932          | 0.7851    | 0.8234 | 0.8038 | 0.9090   |
| 0.2046        | 5.14  | 10000 | 0.2998          | 0.7931    | 0.8215 | 0.8071 | 0.9117   |
| 0.1909        | 5.66  | 11000 | 0.3029          | 0.7925    | 0.8240 | 0.8080 | 0.9112   |
| 0.1857        | 6.17  | 12000 | 0.3160          | 0.7903    | 0.8228 | 0.8062 | 0.9108   |
| 0.1744        | 6.68  | 13000 | 0.3099          | 0.7858    | 0.8259 | 0.8054 | 0.9115   |
| 0.1686        | 7.2   | 14000 | 0.3199          | 0.7859    | 0.8246 | 0.8048 | 0.9097   |
| 0.1613        | 7.71  | 15000 | 0.3161          | 0.7941    | 0.8179 | 0.8058 | 0.9121   |
| 0.1538        | 8.23  | 16000 | 0.3294          | 0.7903    | 0.8221 | 0.8059 | 0.9110   |
| 0.1475        | 8.74  | 17000 | 0.3260          | 0.7935    | 0.8248 | 0.8089 | 0.9129   |
| 0.1429        | 9.25  | 18000 | 0.3378          | 0.7958    | 0.8210 | 0.8082 | 0.9130   |
| 0.1369        | 9.77  | 19000 | 0.3402          | 0.7905    | 0.8240 | 0.8069 | 0.9118   |
| 0.1302        | 10.28 | 20000 | 0.3573          | 0.7865    | 0.8269 | 0.8062 | 0.9114   |
| 0.1276        | 10.8  | 21000 | 0.3564          | 0.7924    | 0.8208 | 0.8063 | 0.9117   |
| 0.122         | 11.31 | 22000 | 0.3590          | 0.7939    | 0.8274 | 0.8103 | 0.9130   |
| 0.1181        | 11.83 | 23000 | 0.3660          | 0.7974    | 0.8234 | 0.8102 | 0.9132   |
| 0.1141        | 12.34 | 24000 | 0.3695          | 0.7921    | 0.8208 | 0.8062 | 0.9112   |
| 0.1118        | 12.85 | 25000 | 0.3649          | 0.7942    | 0.8188 | 0.8063 | 0.9114   |
| 0.1081        | 13.37 | 26000 | 0.3781          | 0.7980    | 0.8149 | 0.8064 | 0.9124   |
| 0.1054        | 13.88 | 27000 | 0.3800          | 0.7913    | 0.8179 | 0.8044 | 0.9120   |
| 0.1023        | 14.4  | 28000 | 0.3857          | 0.7942    | 0.8207 | 0.8072 | 0.9128   |
| 0.101         | 14.91 | 29000 | 0.3852          | 0.7940    | 0.8182 | 0.8059 | 0.9124   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

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

distilbert-base-multilingual-NER-naamapdam-fine-tuned
=====================================================


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: 0.3852
* Precision: 0.7940
* Recall: 0.8182
* F1: 0.8059
* Accuracy: 0.9124


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: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 512
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 512
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 512
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3