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# taNER-500-V2

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: 0.4057
- Precision: 0.7870
- Recall: 0.8040
- F1: 0.7954
- Accuracy: 0.9056

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3751        | 0.49  | 1000  | 0.3876          | 0.7294    | 0.7381 | 0.7337 | 0.8758   |
| 0.3211        | 0.99  | 2000  | 0.3530          | 0.7603    | 0.7427 | 0.7514 | 0.8851   |
| 0.2932        | 1.48  | 3000  | 0.3443          | 0.7501    | 0.7757 | 0.7627 | 0.8882   |
| 0.2884        | 1.98  | 4000  | 0.3404          | 0.7553    | 0.7878 | 0.7712 | 0.8907   |
| 0.268         | 2.47  | 5000  | 0.3241          | 0.7705    | 0.7888 | 0.7795 | 0.8959   |
| 0.2638        | 2.96  | 6000  | 0.3246          | 0.7823    | 0.7850 | 0.7836 | 0.8954   |
| 0.246         | 3.46  | 7000  | 0.3175          | 0.7769    | 0.7989 | 0.7878 | 0.8999   |
| 0.2457        | 3.95  | 8000  | 0.3216          | 0.7732    | 0.7934 | 0.7832 | 0.8999   |
| 0.2253        | 4.44  | 9000  | 0.3180          | 0.7792    | 0.7983 | 0.7887 | 0.8995   |
| 0.2271        | 4.94  | 10000 | 0.3250          | 0.7868    | 0.7895 | 0.7882 | 0.8996   |
| 0.2085        | 5.43  | 11000 | 0.3435          | 0.7838    | 0.7967 | 0.7902 | 0.8995   |
| 0.2091        | 5.93  | 12000 | 0.3300          | 0.7855    | 0.7958 | 0.7906 | 0.9009   |
| 0.1927        | 6.42  | 13000 | 0.3272          | 0.7771    | 0.7983 | 0.7876 | 0.9017   |
| 0.1932        | 6.91  | 14000 | 0.3310          | 0.7836    | 0.8060 | 0.7946 | 0.9047   |
| 0.1777        | 7.41  | 15000 | 0.3377          | 0.7882    | 0.8045 | 0.7963 | 0.9052   |
| 0.1785        | 7.9   | 16000 | 0.3406          | 0.7812    | 0.8042 | 0.7925 | 0.9036   |
| 0.1658        | 8.4   | 17000 | 0.3528          | 0.7892    | 0.7992 | 0.7942 | 0.9043   |
| 0.1651        | 8.89  | 18000 | 0.3419          | 0.7914    | 0.8072 | 0.7992 | 0.9068   |
| 0.1549        | 9.38  | 19000 | 0.3600          | 0.7931    | 0.7964 | 0.7948 | 0.9045   |
| 0.1539        | 9.88  | 20000 | 0.3525          | 0.7851    | 0.8091 | 0.7970 | 0.9052   |
| 0.1449        | 10.37 | 21000 | 0.3634          | 0.7881    | 0.7998 | 0.7939 | 0.9046   |
| 0.1436        | 10.86 | 22000 | 0.3736          | 0.7916    | 0.8058 | 0.7986 | 0.9069   |
| 0.1368        | 11.36 | 23000 | 0.3771          | 0.7892    | 0.8020 | 0.7955 | 0.9053   |
| 0.1347        | 11.85 | 24000 | 0.3800          | 0.7861    | 0.8060 | 0.7959 | 0.9045   |
| 0.1281        | 12.35 | 25000 | 0.3911          | 0.7852    | 0.8055 | 0.7952 | 0.9059   |
| 0.1272        | 12.84 | 26000 | 0.3919          | 0.7880    | 0.8005 | 0.7942 | 0.9052   |
| 0.1217        | 13.33 | 27000 | 0.4021          | 0.7887    | 0.7981 | 0.7934 | 0.9050   |
| 0.1202        | 13.83 | 28000 | 0.3959          | 0.7845    | 0.8057 | 0.7950 | 0.9056   |
| 0.1175        | 14.32 | 29000 | 0.4066          | 0.7864    | 0.8031 | 0.7947 | 0.9052   |
| 0.115         | 14.81 | 30000 | 0.4057          | 0.7870    | 0.8040 | 0.7954 | 0.9056   |


### 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-V2", "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-V2
============


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: 0.4057
* Precision: 0.7870
* Recall: 0.8040
* F1: 0.7954
* Accuracy: 0.9056


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
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