cristinalunaj/cbms_mentalhealth_featureselection

Repository of the paper: Mental-Health Topic Classification employing D-vectors of Large Language Models

CBMS_MentalHealth_FeatureSelection

Repository of the paper presented at CBMS2024 conference: Mental-Health Topic Classification employing D-vectors of Large Language Models

Install requirements/packages:

Python 3.10.12

To install the python packages, create a new virtual environment and run:

pip install -r requirements.txt

** If problems installing certain libraries, try to update your pip version: pip3 install --upgrade pip and run again the previous command

Datasets

1) Counseil-Chat

Dataset released under MIT license, if you use this dataset, cite it as:

@misc{bertagnolli2020counsel, title={Counsel chat: Bootstrapping high-quality therapy data}, author={Bertagnolli, Nicolas}, year={2020}, publisher={Towards Data Science. https://towardsdatascience. com/counsel-chat~…} } More information in: (https://huggingface.co/datasets/nbertagnolli/counsel-chat)

• In CBMS_MentalHealth_FeatureSelection/data/counseilChat/dataSplits/complete_all_TopicClassification.csv > We release the splits used in the experiments of the current publication but if you use the dataset you MUST cite the original source posted before.

2) 7Cups

Question and answers from: https://www.7cups.com/qa/ (In process of cleaning and evaluating for being released)

1. Datasets Splits Extraction

The code for generating the splits of the dataset is available in:

src/DSprocessor/TopicChatProcessor.py

Remember to change the parameter, before running it:

ROOT_PATH = "../../CBMS_MentalHealth_FeatureSelection"

In case of problems, re-use the data-splits that appear in:

data/counseilChat/dataSplits/complete_all_TopicClassification.csv 

2. Baseline

2.1. Word2Vec

• Select the dataset to use: e.g. (DS = "counseilChat")

• Select the model to use: e.g (MODEL_PATH = 'word2Vec')

• Select the number of features (dimension of the embeddings) to use with Word2Vec: n_embs = 768

  python3 CBMS_MentalHealth_FeatureSelection/src/models/embs_Experiments/word2Vec.py

3. EXTRACT EMBEDDINGS

3.1. LLAMA FAMILY EMBEDDINGS

Go to src/embs_Experiments/embs_Llama.py

• Select the dataset to use: e.g. (DS = "counseilChat")
• Select the model to use: e.g (MODEL_PATH = 'meta-llama/Llama-2-7b-chat-hf')
• Add your own access_token from HuggingFace library (if the model requires it)
• Optionally, you can also modify other parameters to change the prompt (prompt_template), maximum number of tokens (max_num_rokens), plus the configuration to load the LLM:
  • bitesAndBytes_config = { "load_in_4bit": True, "bnb_4bit_use_double_quant": False, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16 }

4. Simple model classifier

Once embeddings are generated, go to src/models/classification.py

• Select the dataset to use: e.g. (DS = "counseilChat")
• Select the model from which we want to use the embeddings to train the classifiers: e.g (MODEL_PATH = 'meta-llama/Llama-2-7b-chat-hf')
• Select the list of models to train. Keys indicate the model from dict_model_param, and the list of values, the hyperparameters to eval for each of the parameter selected in each case (e.g. 1: [0.001], indicates to train an SVC model from sklearn with C=0.001).
  • list_models_params = {1:[0.001,0.01,0.1,1.0,10,100], 2:[0.001,0.01,0.1,1.0,10,100], #5:[0.2,0.4,0.5,0.6,0.8,1.0], 6:[0.001,0.01,0.1,1.0,10,100], 7:[5,10,15,20,25,30], 8:[0], 9:[5,10,15,20,25,30], 10:[5,10,15,20,25,30,40,50,60,70,80,90], 11: ['(80)', '(80,80)', '(80,80,80)'], }

5. Feature Selection & Eval classifiers

Perform feature selection & training of the top classifiers with different number of features:

python3 src/models/embs_Experiments/feature_selection/featureSelection.py 
--dataset_name counseilChat --model_name meta-llama/Llama-2-7b-chat-hf --feature_selection 1

Plot graphics for comparing models:

python3 src/models/embs_Experiments/feature_selection/top_features_study.py

python3 src/models/embs_Experiments/feature_selection/top_ratio_results.py

FAQ

• If you experiment problems with the path, try to change the ROOT_PATH parameter to an absolute path (e.g. ROOT_PATH = "../../CBMS_MentalHealth_FeatureSelection" to ROOT_PATH = "/home/xx/CBMS_MentalHealth_FeatureSelection")
• NLP library requires to install certain extra packages (nltk.install (...) read the error messages and follow the instructions)

Citation:

TO ADD ONCE PUBLISHED WITH THE REST OF THE CODE

License:

Apache 2.0.