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# taNER-500-naamapdam-fine-tuned

This model is a fine-tuned version of [livinNector/tabert-500](https://huggingface.co/livinNector/tabert-500) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3466
- Precision: 0.7858
- Recall: 0.8156
- F1: 0.8004
- Accuracy: 0.9134

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.37          | 0.51  | 1000  | 0.3241          | 0.7491    | 0.7745 | 0.7616 | 0.8951   |
| 0.3118        | 1.03  | 2000  | 0.3120          | 0.7631    | 0.7862 | 0.7745 | 0.8998   |
| 0.2877        | 1.54  | 3000  | 0.2994          | 0.7679    | 0.7918 | 0.7797 | 0.9027   |
| 0.284         | 2.06  | 4000  | 0.2977          | 0.7643    | 0.7960 | 0.7798 | 0.9026   |
| 0.2631        | 2.57  | 5000  | 0.2900          | 0.7716    | 0.8001 | 0.7856 | 0.9040   |
| 0.259         | 3.08  | 6000  | 0.2958          | 0.7739    | 0.8058 | 0.7895 | 0.9050   |
| 0.2426        | 3.6   | 7000  | 0.2850          | 0.7795    | 0.8042 | 0.7917 | 0.9072   |
| 0.2378        | 4.11  | 8000  | 0.2910          | 0.7740    | 0.8069 | 0.7901 | 0.9059   |
| 0.2231        | 4.63  | 9000  | 0.2878          | 0.7788    | 0.8106 | 0.7944 | 0.9077   |
| 0.2188        | 5.14  | 10000 | 0.2941          | 0.7752    | 0.8101 | 0.7923 | 0.9087   |
| 0.2066        | 5.66  | 11000 | 0.2928          | 0.7721    | 0.8147 | 0.7928 | 0.9079   |
| 0.2008        | 6.17  | 12000 | 0.3048          | 0.7798    | 0.8141 | 0.7966 | 0.9088   |
| 0.1902        | 6.68  | 13000 | 0.2987          | 0.7834    | 0.8108 | 0.7969 | 0.9099   |
| 0.1843        | 7.2   | 14000 | 0.3055          | 0.7784    | 0.8137 | 0.7957 | 0.9101   |
| 0.1775        | 7.71  | 15000 | 0.2991          | 0.7762    | 0.8155 | 0.7953 | 0.9096   |
| 0.1694        | 8.23  | 16000 | 0.3117          | 0.7876    | 0.8137 | 0.8004 | 0.9120   |
| 0.1631        | 8.74  | 17000 | 0.3085          | 0.7761    | 0.8210 | 0.7979 | 0.9121   |
| 0.1585        | 9.25  | 18000 | 0.3144          | 0.7851    | 0.8063 | 0.7955 | 0.9108   |
| 0.1528        | 9.77  | 19000 | 0.3086          | 0.7834    | 0.8169 | 0.7998 | 0.9124   |
| 0.1458        | 10.28 | 20000 | 0.3167          | 0.7773    | 0.8208 | 0.7985 | 0.9114   |
| 0.143         | 10.8  | 21000 | 0.3202          | 0.7822    | 0.8134 | 0.7975 | 0.9123   |
| 0.1368        | 11.31 | 22000 | 0.3299          | 0.7798    | 0.8176 | 0.7983 | 0.9112   |
| 0.1337        | 11.83 | 23000 | 0.3369          | 0.7857    | 0.8151 | 0.8002 | 0.9131   |
| 0.1289        | 12.34 | 24000 | 0.3366          | 0.7855    | 0.8148 | 0.7999 | 0.9128   |
| 0.1257        | 12.85 | 25000 | 0.3316          | 0.7837    | 0.8172 | 0.8001 | 0.9129   |
| 0.122         | 13.37 | 26000 | 0.3415          | 0.7880    | 0.8120 | 0.7998 | 0.9136   |
| 0.1191        | 13.88 | 27000 | 0.3414          | 0.7867    | 0.8182 | 0.8021 | 0.9139   |
| 0.1157        | 14.4  | 28000 | 0.3481          | 0.7863    | 0.8174 | 0.8016 | 0.9136   |
| 0.1153        | 14.91 | 29000 | 0.3466          | 0.7858    | 0.8156 | 0.8004 | 0.9134   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "taNER-500-naamapdam-fine-tuned", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

taNER-500-naamapdam-fine-tuned
==============================


This model is a fine-tuned version of livinNector/tabert-500 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3466
* Precision: 0.7858
* Recall: 0.8156
* F1: 0.8004
* Accuracy: 0.9134


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 #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