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# tabert-4k-naamapadam

This model is a fine-tuned version of [livinNector/tabert-4k](https://huggingface.co/livinNector/tabert-4k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2805
- Precision: 0.7758
- Recall: 0.8034
- F1: 0.7894
- Accuracy: 0.9077

## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4467        | 0.05  | 400   | 0.3882          | 0.7144    | 0.6655 | 0.6891 | 0.8755   |
| 0.3775        | 0.1   | 800   | 0.3540          | 0.7122    | 0.7155 | 0.7138 | 0.8845   |
| 0.3571        | 0.15  | 1200  | 0.3432          | 0.7329    | 0.7266 | 0.7297 | 0.8872   |
| 0.3461        | 0.21  | 1600  | 0.3360          | 0.7252    | 0.7368 | 0.7309 | 0.8893   |
| 0.3456        | 0.26  | 2000  | 0.3359          | 0.7388    | 0.7470 | 0.7428 | 0.8896   |
| 0.3318        | 0.31  | 2400  | 0.3298          | 0.7460    | 0.7435 | 0.7447 | 0.8908   |
| 0.326         | 0.36  | 2800  | 0.3255          | 0.7490    | 0.7391 | 0.7440 | 0.8940   |
| 0.3264        | 0.41  | 3200  | 0.3243          | 0.7493    | 0.7605 | 0.7549 | 0.8953   |
| 0.3189        | 0.46  | 3600  | 0.3231          | 0.7305    | 0.7715 | 0.7504 | 0.8936   |
| 0.3119        | 0.51  | 4000  | 0.3125          | 0.7645    | 0.7525 | 0.7584 | 0.8985   |
| 0.3111        | 0.57  | 4400  | 0.3100          | 0.7479    | 0.7729 | 0.7602 | 0.8970   |
| 0.3088        | 0.62  | 4800  | 0.3148          | 0.7510    | 0.7749 | 0.7628 | 0.8966   |
| 0.3047        | 0.67  | 5200  | 0.3089          | 0.7581    | 0.7728 | 0.7654 | 0.8981   |
| 0.3054        | 0.72  | 5600  | 0.3073          | 0.7615    | 0.7709 | 0.7662 | 0.8990   |
| 0.3028        | 0.77  | 6000  | 0.3066          | 0.7466    | 0.7835 | 0.7646 | 0.8984   |
| 0.3007        | 0.82  | 6400  | 0.3035          | 0.7555    | 0.7791 | 0.7671 | 0.8995   |
| 0.2923        | 0.87  | 6800  | 0.3004          | 0.7647    | 0.7829 | 0.7737 | 0.9008   |
| 0.2927        | 0.93  | 7200  | 0.3050          | 0.7700    | 0.7646 | 0.7673 | 0.9002   |
| 0.2949        | 0.98  | 7600  | 0.2979          | 0.7686    | 0.7723 | 0.7704 | 0.9014   |
| 0.2758        | 1.03  | 8000  | 0.3013          | 0.7713    | 0.7783 | 0.7748 | 0.9030   |
| 0.2699        | 1.08  | 8400  | 0.3019          | 0.7503    | 0.7997 | 0.7742 | 0.9017   |
| 0.2688        | 1.13  | 8800  | 0.3002          | 0.7593    | 0.7940 | 0.7762 | 0.9017   |
| 0.2625        | 1.18  | 9200  | 0.2926          | 0.7590    | 0.7941 | 0.7762 | 0.9033   |
| 0.2671        | 1.23  | 9600  | 0.2922          | 0.7640    | 0.8019 | 0.7825 | 0.9043   |
| 0.267         | 1.29  | 10000 | 0.2895          | 0.7719    | 0.7877 | 0.7797 | 0.9044   |
| 0.2611        | 1.34  | 10400 | 0.2897          | 0.7704    | 0.7978 | 0.7839 | 0.9053   |
| 0.2666        | 1.39  | 10800 | 0.2896          | 0.7688    | 0.7887 | 0.7786 | 0.9042   |
| 0.2563        | 1.44  | 11200 | 0.2894          | 0.7672    | 0.7981 | 0.7823 | 0.9045   |
| 0.2598        | 1.49  | 11600 | 0.2841          | 0.7705    | 0.7960 | 0.7831 | 0.9058   |
| 0.2549        | 1.54  | 12000 | 0.2854          | 0.7695    | 0.7975 | 0.7832 | 0.9065   |
| 0.2558        | 1.59  | 12400 | 0.2873          | 0.7619    | 0.8108 | 0.7856 | 0.9045   |
| 0.2564        | 1.65  | 12800 | 0.2863          | 0.7757    | 0.7897 | 0.7826 | 0.9062   |
| 0.2618        | 1.7   | 13200 | 0.2860          | 0.7778    | 0.7899 | 0.7838 | 0.9066   |
| 0.2659        | 1.75  | 13600 | 0.2831          | 0.7748    | 0.8013 | 0.7879 | 0.9073   |
| 0.254         | 1.8   | 14000 | 0.2811          | 0.7761    | 0.7978 | 0.7868 | 0.9079   |
| 0.2628        | 1.85  | 14400 | 0.2807          | 0.7713    | 0.8028 | 0.7868 | 0.9069   |
| 0.2552        | 1.9   | 14800 | 0.2806          | 0.7756    | 0.7990 | 0.7872 | 0.9077   |
| 0.2568        | 1.95  | 15200 | 0.2805          | 0.7758    | 0.8034 | 0.7894 | 0.9077   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tabert-4k-naamapadam", "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 

tabert-4k-naamapadam
====================


This model is a fine-tuned version of livinNector/tabert-4k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2805
* Precision: 0.7758
* Recall: 0.8034
* F1: 0.7894
* Accuracy: 0.9077


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: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* 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: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3