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# tabert-1k-naamapadam

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: 0.2825
- Precision: 0.7764
- Recall: 0.8055
- F1: 0.7907
- Accuracy: 0.9068

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4618        | 0.05  | 400   | 0.3963          | 0.7329    | 0.6498 | 0.6889 | 0.8716   |
| 0.3869        | 0.1   | 800   | 0.3583          | 0.7145    | 0.7347 | 0.7244 | 0.8828   |
| 0.3642        | 0.15  | 1200  | 0.3511          | 0.7241    | 0.7412 | 0.7325 | 0.8842   |
| 0.3533        | 0.21  | 1600  | 0.3451          | 0.7393    | 0.7429 | 0.7411 | 0.8873   |
| 0.3501        | 0.26  | 2000  | 0.3367          | 0.7456    | 0.7562 | 0.7509 | 0.8899   |
| 0.3369        | 0.31  | 2400  | 0.3343          | 0.7476    | 0.7549 | 0.7512 | 0.8909   |
| 0.3302        | 0.36  | 2800  | 0.3282          | 0.7413    | 0.7584 | 0.7497 | 0.8926   |
| 0.3327        | 0.41  | 3200  | 0.3238          | 0.7584    | 0.7717 | 0.7650 | 0.8961   |
| 0.3248        | 0.46  | 3600  | 0.3209          | 0.7468    | 0.7795 | 0.7628 | 0.8956   |
| 0.3175        | 0.51  | 4000  | 0.3140          | 0.7659    | 0.7681 | 0.7670 | 0.8985   |
| 0.3132        | 0.57  | 4400  | 0.3111          | 0.7537    | 0.7795 | 0.7664 | 0.8970   |
| 0.3141        | 0.62  | 4800  | 0.3122          | 0.7529    | 0.7797 | 0.7661 | 0.8972   |
| 0.3077        | 0.67  | 5200  | 0.3138          | 0.7493    | 0.7844 | 0.7665 | 0.8974   |
| 0.309         | 0.72  | 5600  | 0.3099          | 0.7674    | 0.7729 | 0.7702 | 0.8992   |
| 0.3085        | 0.77  | 6000  | 0.3038          | 0.7626    | 0.7940 | 0.7780 | 0.9009   |
| 0.3031        | 0.82  | 6400  | 0.3055          | 0.7633    | 0.7834 | 0.7732 | 0.8992   |
| 0.2958        | 0.87  | 6800  | 0.3054          | 0.7621    | 0.7924 | 0.7770 | 0.8991   |
| 0.2953        | 0.93  | 7200  | 0.3076          | 0.7714    | 0.7834 | 0.7774 | 0.9005   |
| 0.2978        | 0.98  | 7600  | 0.3003          | 0.7729    | 0.7855 | 0.7792 | 0.9017   |
| 0.2826        | 1.03  | 8000  | 0.3016          | 0.7665    | 0.7905 | 0.7783 | 0.9012   |
| 0.2757        | 1.08  | 8400  | 0.3053          | 0.7520    | 0.8072 | 0.7786 | 0.8996   |
| 0.2751        | 1.13  | 8800  | 0.3026          | 0.7626    | 0.7982 | 0.7800 | 0.9008   |
| 0.2694        | 1.18  | 9200  | 0.2957          | 0.7682    | 0.8007 | 0.7841 | 0.9039   |
| 0.2723        | 1.23  | 9600  | 0.2944          | 0.7698    | 0.8005 | 0.7849 | 0.9039   |
| 0.2726        | 1.29  | 10000 | 0.2912          | 0.7774    | 0.7930 | 0.7851 | 0.9042   |
| 0.2674        | 1.34  | 10400 | 0.2912          | 0.7739    | 0.7973 | 0.7854 | 0.9043   |
| 0.2714        | 1.39  | 10800 | 0.2907          | 0.7729    | 0.7995 | 0.7860 | 0.9036   |
| 0.2625        | 1.44  | 11200 | 0.2949          | 0.7716    | 0.7965 | 0.7838 | 0.9041   |
| 0.2669        | 1.49  | 11600 | 0.2883          | 0.7701    | 0.8087 | 0.7889 | 0.9054   |
| 0.2601        | 1.54  | 12000 | 0.2868          | 0.7759    | 0.8069 | 0.7911 | 0.9066   |
| 0.2633        | 1.59  | 12400 | 0.2895          | 0.7659    | 0.8125 | 0.7885 | 0.9051   |
| 0.2641        | 1.65  | 12800 | 0.2878          | 0.7790    | 0.7972 | 0.7880 | 0.9059   |
| 0.2661        | 1.7   | 13200 | 0.2875          | 0.7800    | 0.7999 | 0.7898 | 0.9068   |
| 0.2719        | 1.75  | 13600 | 0.2853          | 0.7783    | 0.8025 | 0.7902 | 0.9070   |
| 0.2602        | 1.8   | 14000 | 0.2827          | 0.7801    | 0.8051 | 0.7924 | 0.9070   |
| 0.2688        | 1.85  | 14400 | 0.2819          | 0.7742    | 0.8061 | 0.7898 | 0.9066   |
| 0.2615        | 1.9   | 14800 | 0.2828          | 0.7764    | 0.8017 | 0.7888 | 0.9065   |
| 0.2623        | 1.95  | 15200 | 0.2825          | 0.7764    | 0.8055 | 0.7907 | 0.9068   |


### 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-1k-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-1k-naamapadam
====================


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: 0.2825
* Precision: 0.7764
* Recall: 0.8055
* F1: 0.7907
* Accuracy: 0.9068


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