metadata

license: mit
language:
  - en
metrics:
  - accuracy
  - mse
  - f1
base_model:
  - dmis-lab/biobert-base-cased-v1.2
  - google-bert/bert-base-cased
pipeline_tag: text-classification
model-index:
  - name: bert-causation-rating-dr1
    results:
      - task:
          type: text-classification
        dataset:
          name: rating_dr1
          type: dataset
        metrics:
          - name: off by 1 accuracy
            type: accuracy
            value: 88.13559322033898
          - name: mean squared error for ordinal data
            type: mse
            value: 0.11864406779661017
          - name: weighted F1 score
            type: f1
            value: 0.8787637088733798
          - name: Kendall's tau coefficient
            type: Kendall's tau
            value: 0.922792113501029
        source:
          name: Keling Wang
          url: https://github.com/Keling-Wang
datasets:
  - kelingwang/causation_strength_rating

Model description

This bert-causation-rating-dr1 model is a fine-tuned biobert-base-cased-v1.2 model on a small set of manually annotated texts with causation labels. This model is tasked with classifying a sentence into different levels of strength of causation expressed in this sentence.

The sentences in the dataset were rated independently by two researchers. This dr1 version is tuned on the set of sentences with labels rated by Rater 1.

Intended use and limitations

This model is primarily used to rate for the strength of expressed causation in a sentence extracted from a clinical guideline in the field of diabetes mellitus management. This model predicts strength of causation (SoC) labels based on the text inputs as:

-1: No correlation or variable relationships mentioned in the sentence.
0: There is correlational relationships but not causation in the sentence.
1: The sentence expresses weak causation.
2: The sentence expresses moderate causation.
3: The sentence expresses strong causation. NOTE: The model output is five one-hot logits and will be 0-index based, and the labels will be 0 to 4. It is good to use this python module if one wants to make predictions.

Performance and hyperparameters

Test metrics

This model achieves the following results on the test dataset. The test dataset is a 25% held-out split of the entire dataset with SEED=114514.

Loss: 0.5916
Off-by-1 accuracy: 88.1356
Off-by-2 accuracy: 100.0000
MSE for ordinal data: 0.1186
Weighted F1: 0.8788
Kendall's Tau: 0.9228

This performance is achieved with the following hyperparameters:

Learning rate: 7.94278e-05
Weight decay: 0.111616
Warmup ratio: 0.301057
Power of polynomial learning rate scheduler: 2.619975
Power to the distance measure used in the loss function \alpha: 2.0

Hyperparameter tuning metrics

During the Bayesian optimization procedure for hyperparameter tuning, this model achieves the best target metric (Off-by-1 accuracy) of 99.1147, as the result from 4-fold cross-validation procedure based on best hyperparameters.

Training settings

The following training configurations apply:

seed: 114514
batch_size: 128
epoch: 8
max_length in torch.utils.data.Dataset: 128
Loss function: the OLL loss with a tunable hyperparameter \alpha (Power to the distance measure used in the loss function).
lr: 7.94278e-05
weight_decay: 0.111616
warmup_ratio: 0.301057
lr_scheduler_type: polynomial
lr_scheduler_kwargs: {"power": 2.619975, "lr_end": 1e-8}
Power to the distance measure used in the loss function \alpha: 2.0

Framework versions and devices

This model is run on a NVIDIA P100 CPU provided by Kaggle. Framework versions are:

python==3.10.14
cuda==12.4
NVIDIA-SMI==550.90.07
torch=2.4.0
transformers==4.45.1
scikit-learn==1.2.2
optuna==4.0.0
nlpaug==1.1.11