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# qard

This model is a fine-tuned version of [kaisar-barlybay-sse/kaz_legal_bert](https://huggingface.co/kaisar-barlybay-sse/kaz_legal_bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3614
- Accuracy: 0.4391

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 126  | 1.5970          | 0.4271   |
| No log        | 2.0   | 252  | 1.3113          | 0.4571   |
| No log        | 3.0   | 378  | 1.4418          | 0.4391   |
| 0.838         | 4.0   | 504  | 1.8296          | 0.4491   |
| 0.838         | 5.0   | 630  | 2.2183          | 0.4411   |
| 0.838         | 6.0   | 756  | 1.9106          | 0.4351   |
| 0.838         | 7.0   | 882  | 2.4839          | 0.4451   |
| 0.4281        | 8.0   | 1008 | 3.0046          | 0.4411   |
| 0.4281        | 9.0   | 1134 | 3.2039          | 0.4431   |
| 0.4281        | 10.0  | 1260 | 3.3614          | 0.4391   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "qard", "results": []}]}
transformers pytorch tensorboard bert multiple-choice generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #endpoints_compatible #region-us 

qard
====


This model is a fine-tuned version of kaisar-barlybay-sse/kaz\_legal\_bert on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.3614
* Accuracy: 0.4391


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3