README.md

---
license: cc-by-4.0
datasets:
- gtfintechlab/subjectiveqa
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
---

# SubjECTiveQA-RELEVANT Model

**Model Name:** SubjECTiveQA-RELEVANT

**Model Type:** Text Classification

**Language:** English

**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

**Base Model:** [google-bert/bert-base-uncased](https://huggingface.co/google/bert-base-uncased)

**Dataset Used for Training:** [gtfintechlab/SubjECTive-QA](https://huggingface.co/datasets/gtfintechlab/SubjECTive-QA)

## Model Overview

SubjECTiveQA-RELEVANT is a fine-tuned BERT-based model designed to classify text data according to the 'RELEVANT' attribute. The 'RELEVANT' attribute is one of several subjective attributes annotated in the SubjECTive-QA dataset, which focuses on subjective question-answer pairs in financial contexts.

## Intended Use

This model is intended for researchers and practitioners working on subjective text classification, particularly within financial domains. It is specifically designed to assess the 'RELEVANT' attribute in question-answer pairs, aiding in the analysis of subjective content in financial communications.

## How to Use

To utilize this model, you can load it using the Hugging Face `transformers` library:

```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

# Load the tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/SubjECTiveQA-RELEVANT", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/SubjECTiveQA-RELEVANT", num_labels=3)
config = AutoConfig.from_pretrained("gtfintechlab/SubjECTiveQA-RELEVANT")

# Initialize the text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")

# Classify the 'RELEVANT' attribute in your question-answer pairs
qa_pairs = [
    "Question: What are your company's projections for the next quarter? Answer: We anticipate a 10% increase in revenue due to the launch of our new product line.",
    "Question: Can you explain the recent decline in stock prices? Answer: Market fluctuations are normal, and we are confident in our long-term strategy."
]
results = classifier(qa_pairs, batch_size=128, truncation="only_first")

print(results)
```

In this script:

- **Tokenizer and Model Loading:** The `AutoTokenizer` and `AutoModelForSequenceClassification` classes load the pre-trained tokenizer and model, respectively, from the `gtfintechlab/SubjECTiveQA-RELEVANT` repository.

- **Configuration:** The `AutoConfig` class loads the model configuration, which includes parameters such as the number of labels.

- **Pipeline Initialization:** The `pipeline` function initializes a text classification pipeline with the loaded model, tokenizer, and configuration.

- **Classification:** The `classifier` processes a list of question-answer pairs to assess the 'RELEVANT' attribute. The `batch_size` parameter controls the number of samples processed simultaneously, and `truncation="only_first"` ensures that only the first sequence in each pair is truncated if it exceeds the model's maximum input length.

Ensure that your environment has the necessary dependencies installed.

## Label Interpretation

- **LABEL_0:** Negatively Demonstrative of 'RELEVANT' (0)  
  Indicates that the response lacks relevance.

- **LABEL_1:** Neutral Demonstration of 'RELEVANT' (1)  
  Indicates that the response has an average level of relevance.

- **LABEL_2:** Positively Demonstrative of 'RELEVANT' (2)  
  Indicates that the response is highly relevant.

## Training Data

The model was trained on the SubjECTive-QA dataset, which comprises question-answer pairs from financial contexts, annotated with various subjective attributes, including 'RELEVANT'. The dataset is divided into training, validation, and test sets, facilitating robust model training and evaluation.

## Citation

If you use this model in your research, please cite the SubjECTive-QA dataset:

```
@article{SubjECTiveQA,
  title={SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts’ QA Through Six-Dimensional Feature Analysis},
  author={Huzaifa Pardawala, Siddhant Sukhani, Agam Shah, Veer Kejriwal, Abhishek Pillai, Rohan Bhasin, Andrew DiBiasio, Tarun Mandapati, Dhruv Adha, Sudheer Chava},
  journal={arXiv preprint arXiv:2410.20651},
  year={2024}
}
```

For more details, refer to the [SubjECTive-QA dataset documentation](https://huggingface.co/datasets/gtfintechlab/SubjECTive-QA).

## Contact

For any SubjECTive-QA 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