|
--- |
|
license: mit |
|
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: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
|
model-index: |
|
- name: PubMedBert-PubMed200kRCT |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# PubMedBert-PubMed200kRCT |
|
|
|
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 [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2833 |
|
- Accuracy: 0.8942 |
|
|
|
## 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/PubMedBert-PubMed200kRCT") |
|
tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBert-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': 0.0028450002428144217}, |
|
{'label': 'CONCLUSIONS', 'score': 0.2581048607826233}, |
|
{'label': 'METHODS', 'score': 0.015086210332810879}, |
|
{'label': 'OBJECTIVE', 'score': 0.0016815993003547192}, |
|
{'label': 'RESULTS', 'score': 0.7222822904586792}]] |
|
``` |
|
|
|
## 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 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
| 0.3604 | 0.14 | 5000 | 0.3162 | 0.8821 | |
|
| 0.3326 | 0.29 | 10000 | 0.3112 | 0.8843 | |
|
| 0.3293 | 0.43 | 15000 | 0.3044 | 0.8870 | |
|
| 0.3246 | 0.58 | 20000 | 0.3040 | 0.8871 | |
|
| 0.32 | 0.72 | 25000 | 0.2969 | 0.8888 | |
|
| 0.3143 | 0.87 | 30000 | 0.2929 | 0.8903 | |
|
| 0.3095 | 1.01 | 35000 | 0.2917 | 0.8899 | |
|
| 0.2844 | 1.16 | 40000 | 0.2957 | 0.8886 | |
|
| 0.2778 | 1.3 | 45000 | 0.2943 | 0.8906 | |
|
| 0.2779 | 1.45 | 50000 | 0.2890 | 0.8935 | |
|
| 0.2752 | 1.59 | 55000 | 0.2881 | 0.8919 | |
|
| 0.2736 | 1.74 | 60000 | 0.2835 | 0.8944 | |
|
| 0.2725 | 1.88 | 65000 | 0.2833 | 0.8942 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.18.0.dev0 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.18.3 |
|
- 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} |
|
} |
|
``` --> |
|
|
