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# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0005
- Accuracy: 0.54

## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 57   | 1.3510          | 0.54     |
| No log        | 2.0   | 114  | 0.9606          | 0.54     |
| No log        | 3.0   | 171  | 0.9693          | 0.54     |
| No log        | 4.0   | 228  | 1.0445          | 0.54     |
| No log        | 5.0   | 285  | 1.0005          | 0.54     |


### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
========================================================================


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0005
* Accuracy: 0.54


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: 0.003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3