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# mBERT-naamapdam-fine-tuned

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: 0.4625
- Precision: 0.8060
- Recall: 0.8246
- F1: 0.8152
- Accuracy: 0.9173

## 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: 128
- eval_batch_size: 256
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3625        | 0.26  | 1000  | 0.3300          | 0.7651    | 0.7809 | 0.7729 | 0.8964   |
| 0.3099        | 0.51  | 2000  | 0.3070          | 0.7708    | 0.8041 | 0.7871 | 0.9002   |
| 0.2954        | 0.77  | 3000  | 0.2962          | 0.7793    | 0.8036 | 0.7913 | 0.9041   |
| 0.283         | 1.03  | 4000  | 0.2958          | 0.7843    | 0.8153 | 0.7995 | 0.9066   |
| 0.265         | 1.29  | 5000  | 0.2873          | 0.7930    | 0.8065 | 0.7997 | 0.9069   |
| 0.2613        | 1.54  | 6000  | 0.2838          | 0.7789    | 0.8289 | 0.8031 | 0.9092   |
| 0.2635        | 1.8   | 7000  | 0.2790          | 0.7902    | 0.8252 | 0.8073 | 0.9088   |
| 0.2574        | 2.06  | 8000  | 0.2946          | 0.7887    | 0.8345 | 0.8110 | 0.9098   |
| 0.2355        | 2.31  | 9000  | 0.2859          | 0.7975    | 0.8152 | 0.8063 | 0.9105   |
| 0.2361        | 2.57  | 10000 | 0.2806          | 0.7883    | 0.8313 | 0.8092 | 0.9104   |
| 0.2361        | 2.83  | 11000 | 0.2805          | 0.7931    | 0.8279 | 0.8101 | 0.9123   |
| 0.2268        | 3.08  | 12000 | 0.2934          | 0.7959    | 0.8323 | 0.8137 | 0.9130   |
| 0.2106        | 3.34  | 13000 | 0.2862          | 0.7934    | 0.8311 | 0.8118 | 0.9121   |
| 0.2106        | 3.6   | 14000 | 0.2876          | 0.8009    | 0.8332 | 0.8167 | 0.9143   |
| 0.2131        | 3.86  | 15000 | 0.2777          | 0.8015    | 0.8242 | 0.8127 | 0.9123   |
| 0.1993        | 4.11  | 16000 | 0.2999          | 0.7920    | 0.8311 | 0.8111 | 0.9113   |
| 0.1872        | 4.37  | 17000 | 0.2984          | 0.8003    | 0.8365 | 0.8180 | 0.9143   |
| 0.1861        | 4.63  | 18000 | 0.2894          | 0.7976    | 0.8321 | 0.8145 | 0.9151   |
| 0.1916        | 4.88  | 19000 | 0.2909          | 0.7958    | 0.8300 | 0.8125 | 0.9143   |
| 0.1745        | 5.14  | 20000 | 0.3075          | 0.7906    | 0.8386 | 0.8139 | 0.9136   |
| 0.1649        | 5.4   | 21000 | 0.2986          | 0.8055    | 0.8199 | 0.8127 | 0.9147   |
| 0.1678        | 5.66  | 22000 | 0.3043          | 0.7988    | 0.8303 | 0.8142 | 0.9147   |
| 0.1688        | 5.91  | 23000 | 0.2950          | 0.8026    | 0.8269 | 0.8146 | 0.9155   |
| 0.153         | 6.17  | 24000 | 0.3231          | 0.7995    | 0.8305 | 0.8147 | 0.9150   |
| 0.1468        | 6.43  | 25000 | 0.3145          | 0.7954    | 0.8326 | 0.8136 | 0.9156   |
| 0.1478        | 6.68  | 26000 | 0.3222          | 0.8034    | 0.8307 | 0.8168 | 0.9160   |
| 0.1489        | 6.94  | 27000 | 0.3184          | 0.8019    | 0.8318 | 0.8166 | 0.9161   |
| 0.1311        | 7.2   | 28000 | 0.3336          | 0.8022    | 0.8278 | 0.8148 | 0.9168   |
| 0.1298        | 7.46  | 29000 | 0.3430          | 0.8050    | 0.8281 | 0.8164 | 0.9164   |
| 0.1319        | 7.71  | 30000 | 0.3374          | 0.8005    | 0.8257 | 0.8129 | 0.9152   |
| 0.1312        | 7.97  | 31000 | 0.3320          | 0.8019    | 0.8353 | 0.8183 | 0.9173   |
| 0.1144        | 8.23  | 32000 | 0.3539          | 0.8007    | 0.8309 | 0.8155 | 0.9160   |
| 0.1132        | 8.48  | 33000 | 0.3581          | 0.7940    | 0.8376 | 0.8152 | 0.9158   |
| 0.1159        | 8.74  | 34000 | 0.3566          | 0.8032    | 0.8355 | 0.8191 | 0.9182   |
| 0.117         | 9.0   | 35000 | 0.3384          | 0.8113    | 0.8205 | 0.8159 | 0.9166   |
| 0.0996        | 9.25  | 36000 | 0.3637          | 0.8060    | 0.8256 | 0.8156 | 0.9166   |
| 0.1004        | 9.51  | 37000 | 0.3687          | 0.8043    | 0.8147 | 0.8095 | 0.9152   |
| 0.1015        | 9.77  | 38000 | 0.3715          | 0.8017    | 0.8359 | 0.8185 | 0.9173   |
| 0.1001        | 10.03 | 39000 | 0.3826          | 0.8047    | 0.8288 | 0.8166 | 0.9174   |
| 0.0874        | 10.28 | 40000 | 0.3857          | 0.8087    | 0.8231 | 0.8158 | 0.9168   |
| 0.0892        | 10.54 | 41000 | 0.3817          | 0.8069    | 0.8221 | 0.8145 | 0.9165   |
| 0.0895        | 10.8  | 42000 | 0.3800          | 0.8107    | 0.8291 | 0.8198 | 0.9183   |
| 0.0868        | 11.05 | 43000 | 0.4099          | 0.8032    | 0.8297 | 0.8162 | 0.9177   |
| 0.0777        | 11.31 | 44000 | 0.4099          | 0.8059    | 0.8255 | 0.8156 | 0.9170   |
| 0.0781        | 11.57 | 45000 | 0.4077          | 0.8044    | 0.8335 | 0.8187 | 0.9186   |
| 0.0779        | 11.83 | 46000 | 0.4172          | 0.8050    | 0.8243 | 0.8145 | 0.9161   |
| 0.0759        | 12.08 | 47000 | 0.4230          | 0.8034    | 0.8244 | 0.8138 | 0.9158   |
| 0.0691        | 12.34 | 48000 | 0.4286          | 0.8048    | 0.8221 | 0.8134 | 0.9162   |
| 0.0676        | 12.6  | 49000 | 0.4251          | 0.8091    | 0.8287 | 0.8188 | 0.9185   |
| 0.0695        | 12.85 | 50000 | 0.4289          | 0.8043    | 0.8284 | 0.8161 | 0.9168   |
| 0.0663        | 13.11 | 51000 | 0.4431          | 0.8060    | 0.8246 | 0.8152 | 0.9168   |
| 0.0618        | 13.37 | 52000 | 0.4484          | 0.8054    | 0.8214 | 0.8133 | 0.9162   |
| 0.0614        | 13.62 | 53000 | 0.4421          | 0.8044    | 0.8230 | 0.8136 | 0.9166   |
| 0.0611        | 13.88 | 54000 | 0.4468          | 0.8066    | 0.8231 | 0.8148 | 0.9166   |
| 0.0582        | 14.14 | 55000 | 0.4606          | 0.8055    | 0.8244 | 0.8148 | 0.9173   |
| 0.0552        | 14.4  | 56000 | 0.4642          | 0.8055    | 0.8274 | 0.8163 | 0.9175   |
| 0.0553        | 14.65 | 57000 | 0.4633          | 0.8083    | 0.8248 | 0.8165 | 0.9175   |
| 0.0556        | 14.91 | 58000 | 0.4625          | 0.8060    | 0.8246 | 0.8152 | 0.9173   |


### 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": "mBERT-naamapdam-fine-tuned", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

mBERT-naamapdam-fine-tuned
==========================


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: 0.4625
* Precision: 0.8060
* Recall: 0.8246
* F1: 0.8152
* Accuracy: 0.9173


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: 128
* eval\_batch\_size: 256
* 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: 128
* eval\_batch\_size: 256
* 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 #bert #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: 128
* eval\_batch\_size: 256
* 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