
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# medqa_fine_tuned

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4462
- Accuracy: 0.4002

## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 1.3208          | 0.3553   |
| 1.2802        | 2.0   | 636  | 1.3428          | 0.3703   |
| 1.2802        | 3.0   | 954  | 1.3780          | 0.3892   |
| 1.1466        | 4.0   | 1272 | 1.4234          | 0.3978   |
| 1.052         | 5.0   | 1590 | 1.4462          | 0.4002   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0

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

medqa\_fine\_tuned
==================


This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4462
* Accuracy: 0.4002


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-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.3.2
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.3.2
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.3.2
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.3.2
* Tokenizers 0.11.0