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# OTE-ABSA-Qarib-DAPT-LABR-run1

This model is a fine-tuned version of [salohnana2018/Qarib-domianAdaption-OTE-ABSA-LABR](https://huggingface.co/salohnana2018/Qarib-domianAdaption-OTE-ABSA-LABR) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1353
- Precision: 0.7645
- Recall: 0.7762
- F1: 0.7703
- Accuracy: 0.9509

## 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: 32
- eval_batch_size: 8
- seed: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2003        | 1.0   | 121  | 0.1354          | 0.7193    | 0.7970 | 0.7562 | 0.9461   |
| 0.1047        | 2.0   | 242  | 0.1262          | 0.8056    | 0.7213 | 0.7611 | 0.9519   |
| 0.0754        | 3.0   | 363  | 0.1353          | 0.7645    | 0.7762 | 0.7703 | 0.9509   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "OTE-ABSA-Qarib-DAPT-LABR-run1", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

OTE-ABSA-Qarib-DAPT-LABR-run1
=============================


This model is a fine-tuned version of salohnana2018/Qarib-domianAdaption-OTE-ABSA-LABR on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1353
* Precision: 0.7645
* Recall: 0.7762
* F1: 0.7703
* Accuracy: 0.9509


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: 32
* eval\_batch\_size: 8
* seed: 25
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 25
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #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: 32
* eval\_batch\_size: 8
* seed: 25
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3