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# pubmedqa_roberta

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the pubmed_qa dataset.

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.79  | 2    | 1.0976          | 0.552    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.11.0

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pubmed_qa"], "model-index": [{"name": "pubmedqa_roberta", "results": []}]}
transformers pytorch roberta multiple-choice generated_from_trainer dataset:pubmed_qa license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-pubmed_qa #license-mit #endpoints_compatible #region-us 

pubmedqa\_roberta
=================


This model is a fine-tuned version of roberta-base on the pubmed\_qa dataset.


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 25
* total\_train\_batch\_size: 200
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 25
* total\_train\_batch\_size: 200
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-pubmed_qa #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 25
* total\_train\_batch\_size: 200
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-pubmed_qa #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 25
* total\_train\_batch\_size: 200
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.11.0