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# taNER-1k-V2

This model is a fine-tuned version of [livinNector/tabert-1k](https://huggingface.co/livinNector/tabert-1k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4253
- Precision: 0.7866
- Recall: 0.8029
- F1: 0.7947
- Accuracy: 0.9049

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3724        | 0.49  | 1000  | 0.3865          | 0.7280    | 0.7372 | 0.7326 | 0.8758   |
| 0.3199        | 0.99  | 2000  | 0.3516          | 0.7524    | 0.7561 | 0.7543 | 0.8858   |
| 0.2911        | 1.48  | 3000  | 0.3436          | 0.7543    | 0.7765 | 0.7653 | 0.8906   |
| 0.2867        | 1.98  | 4000  | 0.3391          | 0.7522    | 0.7908 | 0.7710 | 0.8909   |
| 0.2654        | 2.47  | 5000  | 0.3262          | 0.7696    | 0.7845 | 0.7770 | 0.8961   |
| 0.2616        | 2.96  | 6000  | 0.3294          | 0.7784    | 0.7800 | 0.7792 | 0.8954   |
| 0.2422        | 3.46  | 7000  | 0.3191          | 0.7779    | 0.7934 | 0.7856 | 0.8999   |
| 0.2422        | 3.95  | 8000  | 0.3272          | 0.7735    | 0.7962 | 0.7847 | 0.8985   |
| 0.2208        | 4.44  | 9000  | 0.3252          | 0.7811    | 0.7952 | 0.7881 | 0.9012   |
| 0.2227        | 4.94  | 10000 | 0.3220          | 0.7789    | 0.7993 | 0.7890 | 0.9026   |
| 0.204         | 5.43  | 11000 | 0.3413          | 0.7904    | 0.7894 | 0.7899 | 0.9007   |
| 0.2036        | 5.93  | 12000 | 0.3329          | 0.7810    | 0.7984 | 0.7896 | 0.9009   |
| 0.1874        | 6.42  | 13000 | 0.3362          | 0.7872    | 0.7986 | 0.7929 | 0.9033   |
| 0.1877        | 6.91  | 14000 | 0.3414          | 0.7764    | 0.8029 | 0.7894 | 0.9013   |
| 0.172         | 7.41  | 15000 | 0.3463          | 0.7871    | 0.7997 | 0.7933 | 0.9032   |
| 0.1729        | 7.9   | 16000 | 0.3441          | 0.7863    | 0.8001 | 0.7931 | 0.9034   |
| 0.159         | 8.4   | 17000 | 0.3625          | 0.7856    | 0.7970 | 0.7912 | 0.9019   |
| 0.1585        | 8.89  | 18000 | 0.3575          | 0.7867    | 0.7980 | 0.7923 | 0.9030   |
| 0.1485        | 9.38  | 19000 | 0.3761          | 0.7850    | 0.7965 | 0.7907 | 0.9029   |
| 0.1468        | 9.88  | 20000 | 0.3658          | 0.7874    | 0.8019 | 0.7946 | 0.9037   |
| 0.1378        | 10.37 | 21000 | 0.3835          | 0.7851    | 0.8039 | 0.7944 | 0.9042   |
| 0.1364        | 10.86 | 22000 | 0.3852          | 0.7861    | 0.8019 | 0.7940 | 0.9043   |
| 0.1294        | 11.36 | 23000 | 0.3906          | 0.7854    | 0.7973 | 0.7913 | 0.9038   |
| 0.1277        | 11.85 | 24000 | 0.3947          | 0.7875    | 0.7988 | 0.7931 | 0.9030   |
| 0.1207        | 12.35 | 25000 | 0.4082          | 0.7841    | 0.7997 | 0.7918 | 0.9035   |
| 0.1199        | 12.84 | 26000 | 0.4137          | 0.7888    | 0.7993 | 0.7940 | 0.9049   |
| 0.1144        | 13.33 | 27000 | 0.4155          | 0.7875    | 0.7996 | 0.7935 | 0.9046   |
| 0.113         | 13.83 | 28000 | 0.4177          | 0.7840    | 0.8053 | 0.7945 | 0.9046   |
| 0.1103        | 14.32 | 29000 | 0.4280          | 0.7867    | 0.8021 | 0.7943 | 0.9042   |
| 0.1078        | 14.81 | 30000 | 0.4253          | 0.7866    | 0.8029 | 0.7947 | 0.9049   |


### 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-1k-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-1k-V2
===========


This model is a fine-tuned version of livinNector/tabert-1k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4253
* Precision: 0.7866
* Recall: 0.8029
* F1: 0.7947
* Accuracy: 0.9049


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