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# multiple_answer_QA

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7334
- Accuracy: 0.2675

## 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: 2e-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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 175  | 1.3886          | 0.23     |
| No log        | 2.0   | 350  | 1.3892          | 0.255    |
| 1.3936        | 3.0   | 525  | 1.3863          | 0.2375   |
| 1.3936        | 4.0   | 700  | 1.3868          | 0.255    |
| 1.3936        | 5.0   | 875  | 1.4511          | 0.26     |
| 1.2973        | 6.0   | 1050 | 1.6738          | 0.24     |
| 1.2973        | 7.0   | 1225 | 1.9684          | 0.265    |
| 1.2973        | 8.0   | 1400 | 2.5002          | 0.28     |
| 0.6916        | 9.0   | 1575 | 2.9872          | 0.28     |
| 0.6916        | 10.0  | 1750 | 3.2354          | 0.28     |
| 0.6916        | 11.0  | 1925 | 3.6618          | 0.3025   |
| 0.2428        | 12.0  | 2100 | 4.1750          | 0.275    |
| 0.2428        | 13.0  | 2275 | 4.1384          | 0.275    |
| 0.2428        | 14.0  | 2450 | 4.5173          | 0.26     |
| 0.1118        | 15.0  | 2625 | 4.6013          | 0.275    |
| 0.1118        | 16.0  | 2800 | 4.2549          | 0.2525   |
| 0.1118        | 17.0  | 2975 | 5.2751          | 0.275    |
| 0.0482        | 18.0  | 3150 | 4.9489          | 0.275    |
| 0.0482        | 19.0  | 3325 | 5.6077          | 0.28     |
| 0.0261        | 20.0  | 3500 | 5.3054          | 0.2625   |
| 0.0261        | 21.0  | 3675 | 5.1955          | 0.2625   |
| 0.0261        | 22.0  | 3850 | 5.6210          | 0.2575   |
| 0.0175        | 23.0  | 4025 | 5.6576          | 0.25     |
| 0.0175        | 24.0  | 4200 | 5.6687          | 0.2725   |
| 0.0175        | 25.0  | 4375 | 5.6082          | 0.255    |
| 0.0133        | 26.0  | 4550 | 5.5999          | 0.27     |
| 0.0133        | 27.0  | 4725 | 5.7972          | 0.26     |
| 0.0133        | 28.0  | 4900 | 5.6427          | 0.2575   |
| 0.0089        | 29.0  | 5075 | 5.7253          | 0.2675   |
| 0.0089        | 30.0  | 5250 | 5.7334          | 0.2675   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "multiple_answer_QA", "results": []}]}
transformers pytorch tensorboard bert multiple-choice generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

multiple\_answer\_QA
====================


This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 5.7334
* Accuracy: 0.2675


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: 2e-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: 30


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 30 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 30 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 30### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3