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# qamodel2

This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7777

## 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: 1e-06
- 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9544        | 1.0   | 81   | 2.0493          |
| 1.7079        | 2.0   | 162  | 1.8907          |
| 1.6135        | 3.0   | 243  | 1.8236          |
| 1.5829        | 4.0   | 324  | 1.7984          |
| 1.5381        | 5.0   | 405  | 1.7900          |
| 1.515         | 6.0   | 486  | 1.7827          |
| 1.5079        | 7.0   | 567  | 1.7827          |
| 1.4952        | 8.0   | 648  | 1.7777          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["subjqa"], "model-index": [{"name": "qamodel2", "results": []}]}
transformers pytorch tensorboard roberta question-answering generated_from_trainer dataset:subjqa license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-subjqa #license-cc-by-4.0 #endpoints_compatible #region-us 

qamodel2
========


This model is a fine-tuned version of deepset/roberta-base-squad2 on the subjqa dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7777


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: 1e-06
* 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: 8


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* 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: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-subjqa #license-cc-by-4.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* 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: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-subjqa #license-cc-by-4.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* 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: 8### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2