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tags: []
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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##
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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##
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license: cc-by-nc-sa-4.0
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datasets:
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- gtfintechlab/central_bank_of_chile
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language:
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- en
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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base_model:
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- roberta-base
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pipeline_tag: text-classification
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library_name: transformers
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---
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# World of Central Banks Model
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**Model Name:** Central Bank of Chile Stance Detection Model
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**Model Type:** Text Classification
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**Language:** English
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**License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
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**Base Model:** [roberta-base](https://huggingface.co/FacebookAI/roberta-base)
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**Dataset Used for Training:** [gtfintechlab/central_bank_of_chile](https://huggingface.co/datasets/gtfintechlab/central_bank_of_chile)
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## Model Overview
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Central Bank of Chile 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_bank_of_chile dataset, which focuses on meeting minutes for the Central Bank of Chile.
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## Intended Use
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This model is intended for researchers and practitioners working on subjective text classification for the Central Bank of Chile, 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.
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## How to Use
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To utilize this model, load it using the Hugging Face `transformers` library:
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```python
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
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# Load tokenizer, model, and configuration
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tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/central_bank_of_chile", do_lower_case=True, do_basic_tokenize=True)
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model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/central_bank_of_chile", num_labels=4)
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config = AutoConfig.from_pretrained("gtfintechlab/central_bank_of_chile")
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# Initialize text classification pipeline
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classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")
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# Classify Stance Detection
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sentences = [
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"[Sentence 1]",
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"[Sentence 2]"
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]
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results = classifier(sentences, batch_size=128, truncation="only_first")
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print(results)
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```
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In this script:
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- **Tokenizer and Model Loading:**
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Loads the pre-trained tokenizer and model from `gtfintechlab/central_bank_of_chile`.
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- **Configuration:**
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Loads model configuration parameters, including the number of labels.
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- **Pipeline Initialization:**
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Initializes a text classification pipeline with the model, tokenizer, and configuration.
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- **Classification:**
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Labels sentences based on **Stance Detection**.
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Ensure your environment has the necessary dependencies installed.
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## Label Interpretation
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- **LABEL_0:** Hawkish; the sentnece supports contractionary monetary policy.
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- **LABEL_1:** Dovish; the sentence supports expansionary monetary policy.
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- **LABEL_2:** Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment.
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- **LABEL_3:** Irrelevant; the sentence is not related to monetary policy.
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## Training Data
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The model was trained on the central_bank_of_chile dataset, comprising annotated sentences from the Central Bank of Chile meeting minutes, labeled by **Stance Detection**. The dataset includes training, validation, and test splits.
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## Citation
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If you use this model in your research, please cite the central_bank_of_chile:
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```bibtex
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@article{WCBShahSukhaniPardawala,
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title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
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author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
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year={2025}
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}
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```
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For more details, refer to the [central_bank_of_chile dataset documentation](https://huggingface.co/gtfintechlab/central_bank_of_chile).
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## Contact
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For any central_bank_of_chile related issues and questions, please contact:
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- Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu
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- Siddhant Sukhani: ssukhani3[at]gatech[dot]edu
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- Agam Shah: ashah482[at]gatech[dot]edu
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