tempsentpolicy 0.0.3

Designed to ensure robust generalization across time stamps for sentiment analysis in policy-related texts

tempsentpolicy

Temporally-sensitive Sentiment analysis in Policy (tempsentpolicy) is a Python package ddesigned to ensure robust generalization across time stamps for sentiment analysis in policy-related texts.

It accounts for the temporal dynamics of evolving policy discourse and evaluates sentiment models under realistic settings that mimic actual policy analysis scenarios.

Key features include:

• Continuous time-series clustering to prioritize data points for annotation based on temporal trends.

• Advanced model merging techniques to unify multiple models fine-tuned on distinct temporal slices.

Methods:

• Continous time series is performed using Ruptures.

• It supports the following merging techniques:

  1. Uniform Souping (souping)

  2. Greedy Souping (greedy_souping)

  3. Task Arithmetic (task_arithmetic)

  4. TIES (ties)

  5. DARE (dare)

  6. Fisher Merging (fisher_merging)

  7. RegMean Merging (regmean_merging)

Requirements

Required Packages

The following Python packages are required:

• matplotlib

• ruptures

• pandas

• tqdm

• torch

• transformers

• datasets

• scikit-learn

• numpy

These will be installed automatically when using pip install.

Alternatively, you could directly install them

pip install matplotlib ruptures pandas tqdm torch transformers datasets scikit-learn numpy

Computational requirements

You require at least one GPU to use tempsentpolicy.

VRAM requirements depend on factors like batch size, model size, etc.

However, at least 12GB of VRAM is recommended

Installation

To install in python, simply do the following:

pip install tempsentpolicy

Quick start

To learn how we used it in our experiments, go to demo.ipynb. Alternatively, refer to this quick guide here:

1. Lets start off by performing continous time series clustering to select our annotated data

from tempsentpolicy import greedy_souping, ties

from tempsentpolicy import finetune

from tempsentpolicy import cluster_sampling, continous_time_series_clustering



df_climate=continous_time_series_clustering(df=df,time_col="timestamp_col",level="M", plot=False, penalty=0.1) # note that level "M" stands for Month



# perform continous time series clustering and selecting based on the clusters

df=cluster_sampling(df=df,sample_size=10000,stratified_col="cluster"):

df_annotated=df[df["selected"]==1].reset_index(drop=True)

2. Now first lets finetune a single unified model

# finetuning

finetune(df=df_annotated, model_name="microsoft/deberta-v3-large",cluster_col_name=None,

         folder_name="naive_finetuning/continous_clustering_by_month/climate_change", # note folder name to save our models

         text_col='message', label_col="sentiment",  label2id={"Anti":0,"Neutral":1,"Pro":2,"News":3},

         learning_rate=1e-5, warmup_ratio=0.05, weight_decay=0.001,

         epochs=3, batch_size=6, early_stopping_patience=2, return_val_data=False)

3. Now first lets finetune individual models (one for each cluster), and merge them with greedy soup.

# finetuning

df_val=finetune(df=df_annotated, model_name="microsoft/deberta-v3-large",

                cluster_col_name="cluster", # the difference is here --> we use "cluster" instead of None

                folder_name="batch_finetuning/continous_clustering_by_month/climate_change", # note folder name to save our models

                text_col='message', 

                label_col="sentiment",  

                label2id={"Anti":0,"Neutral":1,"Pro":2,"News":3},

                learning_rate=1e-5, 

                warmup_ratio=0.05, 

                weight_decay=0.001,

                epochs=8, 

                batch_size=6, 

                early_stopping_patience=2, 

                return_val_data=True) # note TRUE if we want to return validation data. 



# Now we perform greedy souping

greedy_souping(

    models_folder="models/batch_finetuning/continous_clustering_by_month/climate_change/deberta-v3-large", # where the individual models are saved

    save_path="models/merged_models/continous_clustering_by_month/climate_change/greedy_soup/deberta-v3-large", # the merged model will be saved here

    df_val=df_val,  # validation data. 

    col_label="sentiment",

    text_col='message',

    num_labels=4,

    label2id={"Anti":0,"Neutral":1,"Pro":2,"News":3})



# the model will be saved under `save_path="models/merged_models/continous_clustering_by_month/climate_change/greedy_soup/deberta-v3-large"`

NOTE

This is a temporary repository for the purposes of anonymity in the ACL SRW submission. Upon acceptance, it will be converted to a package under the main authors PyPi account. Stay tuned for updates!!!