Recognizing Underlying Patterns in Categorical Data via Symbolization and Masking Mechanisms

- Depending on your transformer toolkit versions, the transformer import code may need to be adjusted, like as follows:
+ from transformers.modeling_bert import BertPreTrainedModel, BertPooler
+ --> from transformers.models.bert.modeling_bert import BertPreTrainedModel, BertPooler
- (Please check your transformer toolikt, and update the import code accordingly.)

How to run the code?

After downloading the code, you can run

python3 run.py

directly for categorical clustering. We suggest adjusting the hyperparameters multiple times to achieve better results.

What are the scripts used for?

(1)LM/BertForMaskedLM: Contains the model structure and configuration of the BERT.

(2)make_dataset: Data processing. Help us prepare the training set.

(3)models: Define the network structure of SAMM.

(4) utils: Contains functions for data processing and model evaluation.

Several toolkits may be needed to run the code

(1) pytorch (https://anaconda.org/pytorch/pytorch)

(2) sklearn (https://anaconda.org/anaconda/scikit-learn)

(3) transformers (https://anaconda.org/conda-forge/transformers)