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# BioBert-PubMed200kRCT

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2832
- Accuracy: 0.8934

## Model description

More information needed

## Intended uses & limitations

The model can be used for text classification tasks of Randomized Controlled Trials that does not have any structure. The text can be classified as one of the following:
* BACKGROUND
* CONCLUSIONS
* METHODS
* OBJECTIVE
* RESULTS

The model can be directly used like this:

```python
from transformers import TextClassificationPipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/BioBert-PubMed200kRCT")
tokenizer = AutoTokenizer.from_pretrained("pritamdeka/BioBert-PubMed200kRCT")
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
pipe("Treatment of 12 healthy female subjects with CDCA for 2 days resulted in increased BAT activity.")
```
Results will be shown as follows:

```python
[[{'label': 'BACKGROUND', 'score': 0.0027583304326981306},
  {'label': 'CONCLUSIONS', 'score': 0.044541116803884506},
  {'label': 'METHODS', 'score': 0.19493348896503448},
  {'label': 'OBJECTIVE', 'score': 0.003996663726866245},
  {'label': 'RESULTS', 'score': 0.7537703514099121}]]
```

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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3587        | 0.14  | 5000  | 0.3137          | 0.8834   |
| 0.3318        | 0.29  | 10000 | 0.3100          | 0.8831   |
| 0.3286        | 0.43  | 15000 | 0.3033          | 0.8864   |
| 0.3236        | 0.58  | 20000 | 0.3037          | 0.8862   |
| 0.3182        | 0.72  | 25000 | 0.2939          | 0.8876   |
| 0.3129        | 0.87  | 30000 | 0.2910          | 0.8885   |
| 0.3078        | 1.01  | 35000 | 0.2914          | 0.8887   |
| 0.2791        | 1.16  | 40000 | 0.2975          | 0.8874   |
| 0.2723        | 1.3   | 45000 | 0.2913          | 0.8906   |
| 0.2724        | 1.45  | 50000 | 0.2879          | 0.8904   |
| 0.27          | 1.59  | 55000 | 0.2874          | 0.8911   |
| 0.2681        | 1.74  | 60000 | 0.2848          | 0.8928   |
| 0.2672        | 1.88  | 65000 | 0.2832          | 0.8934   |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6


## Citing & Authors

<!--- Describe where people can find more information -->

If you use the model kindly cite the following work

```
@inproceedings{deka2022evidence,
  title={Evidence Extraction to Validate Medical Claims in Fake News Detection},
  author={Deka, Pritam and Jurek-Loughrey, Anna and others},
  booktitle={International Conference on Health Information Science},
  pages={3--15},
  year={2022},
  organization={Springer}
}
```
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in paraffin and tested for the presence of abnormal prion protein (PrP)."}], "base_model": "dmis-lab/biobert-base-cased-v1.1", "model-index": [{"name": "BioBert-PubMed200kRCT", "results": []}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer base_model:dmis-lab/biobert-base-cased-v1.1 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us 

BioBert-PubMed200kRCT
=====================


This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the PubMed200kRCT dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2832
* Accuracy: 0.8934


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


The model can be used for text classification tasks of Randomized Controlled Trials that does not have any structure. The text can be classified as one of the following:


* BACKGROUND
* CONCLUSIONS
* METHODS
* OBJECTIVE
* RESULTS


The model can be directly used like this:


Results will be shown as follows:


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6


Citing & Authors
----------------


If you use the model kindly cite the following work



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6


Citing & Authors
----------------


If you use the model kindly cite the following work
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6


Citing & Authors
----------------


If you use the model kindly cite the following work
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6


Citing & Authors
----------------


If you use the model kindly cite the following work