--- license: cc-by-nc-sa-4.0 datasets: - gtfintechlab/central_reserve_bank_of_peru language: - en metrics: - accuracy - f1 - precision - recall base_model: - roberta-base pipeline_tag: text-classification library_name: transformers --- # World of Central Banks Model **Model Name:** Central Reserve Bank of Peru Stance Detection Model **Model Type:** Text Classification **Language:** English **License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) **Base Model:** [roberta-base](https://huggingface.co/FacebookAI/roberta-base) **Dataset Used for Training:** [gtfintechlab/central_reserve_bank_of_peru](https://huggingface.co/datasets/gtfintechlab/central_reserve_bank_of_peru) ## Model Overview Central Reserve Bank of Peru Stance Detection Model is a fine-tuned roberta-base model designed to classify text data on **Stance Detection**. This label is annotated in the central_reserve_bank_of_peru dataset, which focuses on meeting minutes for the Central Reserve Bank of Peru. ## Intended Use This model is intended for researchers and practitioners working on subjective text classification for the Central Reserve Bank of Peru, particularly within financial and economic contexts. It is specifically designed to assess the **Stance Detection** label, aiding in the analysis of subjective content in financial and economic communications. ## How to Use To utilize this model, load it using the Hugging Face `transformers` library: ```python from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig # Load tokenizer, model, and configuration tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/central_reserve_bank_of_peru", do_lower_case=True, do_basic_tokenize=True) model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/central_reserve_bank_of_peru", num_labels=4) config = AutoConfig.from_pretrained("gtfintechlab/central_reserve_bank_of_peru") # Initialize text classification pipeline classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt") # Classify Stance Detection sentences = [ "[Sentence 1]", "[Sentence 2]" ] results = classifier(sentences, batch_size=128, truncation="only_first") print(results) ``` In this script: - **Tokenizer and Model Loading:** Loads the pre-trained tokenizer and model from `gtfintechlab/central_reserve_bank_of_peru`. - **Configuration:** Loads model configuration parameters, including the number of labels. - **Pipeline Initialization:** Initializes a text classification pipeline with the model, tokenizer, and configuration. - **Classification:** Labels sentences based on **Stance Detection**. Ensure your environment has the necessary dependencies installed. ## Label Interpretation - **LABEL_0:** Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment. - **LABEL_1:** Hawkish; the sentnece supports contractionary monetary policy. - **LABEL_2:** Dovish; the sentence supports expansionary monetary policy. - **LABEL_3:** Irrelevant; the sentence is not related to monetary policy. ## Training Data The model was trained on the central_reserve_bank_of_peru dataset, comprising annotated sentences from the Central Reserve Bank of Peru meeting minutes, labeled by **Stance Detection**. The dataset includes training, validation, and test splits. ## Citation If you use this model in your research, please cite the central_reserve_bank_of_peru: ```bibtex @article{WCBShahSukhaniPardawala, title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications}, author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.}, year={2025} } ``` For more details, refer to the [central_reserve_bank_of_peru dataset documentation](https://huggingface.co/datasets/gtfintechlab/central_reserve_bank_of_peru). ## Contact For any Central Reserve Bank of Peru related issues and questions, please contact: - Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu - Siddhant Sukhani: ssukhani3[at]gatech[dot]edu - Agam Shah: ashah482[at]gatech[dot]edu