gtfintechlab
/

model_WCB_time_label

@@ -1,199 +1,118 @@
 ---
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: cc-by-nc-sa-4.0
+datasets:
+- gtfintechlab/WCB
+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:** WCB Temporal Classification 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](https://huggingface.co/FacebookAI/roberta-base)
+**Dataset Used for Training:** [gtfintechlab/WCB_380k_sentences](https://huggingface.co/datasets/gtfintechlab/WCB_380k_sentences)
+## Model Overview
+WCB Temporal Classification Model is a fine-tuned RoBERTa-based model designed to classify text data on **Temporal Classification**. This label is annotated in the model_WCB_time_label dataset, which focuses on meeting minutes for the all 25 central banks, listed in the paper _Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications_.
+## Intended Use
+This model is intended for researchers and practitioners working on subjective text classification, particularly within financial and economic contexts. It is specifically designed to assess the **Temporal Classification** 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/model_WCB_time_label", do_lower_case=True, do_basic_tokenize=True)
+model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/model_WCB_time_label", num_labels=2)
+config = AutoConfig.from_pretrained("gtfintechlab/model_WCB_time_label")
+# Initialize text classification pipeline
+classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")
+# Classify Temporal Classification
+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/model_WCB_time_label`.
+- **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 **Temporal Classification**.
+Ensure your environment has the necessary dependencies installed.
+## Label Interpretation
+- **LABEL_0:** Forward-looking; the sentence discusses future economic events or decisions.
+- **LABEL_1:** Not forward-looking; the sentence discusses past or current economic events or decisions.
+## Training Data
+The model was trained on the model_WCB_time_label dataset, comprising annotated sentences from 25 central banks, labeled by Temporal Classification. The dataset includes training, validation, and test splits.
+## Citation
+If you use this model in your research, please cite the model_WCB_time_label:
+```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 [model_WCB_time_label dataset documentation](https://huggingface.co/gtfintechlab/model_WCB_time_label).
+## Contact
+For any model_WCB_time_label 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