---
license: apache-2.0
language:
- en
metrics:
- exact_match
- f1
base_model:
- google-bert/bert-base-uncased
---
# Fine-Tuned BERT Models for Thermoelectric Materials Question Answering

## Introduction

This repository contains three BERT models fine-tuned for question-answering (QA) tasks related to thermoelectric materials. The models are trained on different datasets to evaluate their performance on specialised QA tasks in the field of materials science.

We present a method for auto-generating a large question-answering dataset about thermoelectric materials for language model applications. The method was used to generate a dataset with sentence-wide contexts from a database of thermoelectric material records. The dataset was contrasted with SQuAD-v2, as well as the mixed combination of the two datasets. Hyperparameter optimisation was employed to fine-tune BERT models on each dataset, and the three best-performing models were then compared on a manually annotated test set of thermoelectric material paragraph contexts with questions spanning material names, five different properties, and temperatures during recording. The best BERT model fine-tuned on the mixed dataset outperforms the other two models when evaluated on the test dataset, indicating that mixing datasets with different semantic and syntactic scopes might be a beneficial approach to improving performance on specialised question-answering tasks.

## Models Included

1. **squad-v2_best**

    Description: Fine-tuned on the SQuAD-v2 dataset, which is a widely used benchmark for QA tasks. \
    Dataset: SQuAD-v2 \
    Location: squad-v2_best/

2. **te-cde_best**

    Description: Fine-tuned on a thermoelectric materials-specific dataset generated using our auto-generation method. \
    Dataset: Thermoelectric Materials QA Dataset (TE-CDE) \
    Location: te-cde_best/

3. **mixed_best**

    Description: Fine-tuned on a mixed dataset combining SQuAD-v2 and the thermoelectric materials dataset to enhance performance on specialised QA tasks. \
    Dataset: Combination of SQuAD-v2 and TE-CDE \
    Location: mixed_best/

## Dataset Details

**SQuAD-v2**

    A reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles.
    Some questions are unanswerable, adding complexity to the QA task.

**Thermoelectric Materials QA Dataset (TE-CDE)**

    Auto-generated dataset containing QA pairs about thermoelectric materials.
    Contexts are sentence-wide excerpts from a database of thermoelectric material records.
    Questions cover:
        Material names
        Five different properties
        Temperatures during recording

**Mixed Dataset**

    A combination of SQuAD-v2 and TE-CDE datasets.
    Aims to leverage the strengths of both general-purpose and domain-specific data.

## Training Details

    Base Model: BERT Base Uncased
    Hyperparameter Optimisation: Employed to find the best-performing models for each dataset.
    Training Parameters:
        Epochs: Adjusted per dataset based on validation loss.
        Batch Size: Optimized during training.
        Learning Rate: Tuned using grid search.

## Evaluation Metrics

    Evaluation Dataset: Manually annotated test set of thermoelectric material paragraph contexts.
    Metrics Used:
        Exact Match (EM): Measures the percentage of predictions that match any one of the ground truth answers exactly.
        F1 Score: Harmonic mean of precision and recall, considering overlap between the prediction and ground truth answers.

### Performance Comparison
Model	Exact Match (EM)	F1 Score
squad-v2_best	57.60%	61.82%
te-cde_best	65.39%	69.78%
mixed_best	67.92%	72.29%

## Usage Instructions

### Installing Dependencies

```bash
pip install transformers
```

### Loading a Model

Replace `model_name` with one of the following:

    "odysie/bert-finetuned-qa-datasets/squad-v2_best"
    "odysie/bert-finetuned-qa-datasets/te-cde_best"
    "odysie/bert-finetuned-qa-datasets/mixed_best"

```python
from transformers import BertForQuestionAnswering, BertTokenizer

model_name = "odysie/bert-finetuned-qa-datasets/mixed_best"

tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForQuestionAnswering.from_pretrained(model_name)

# Example question and context
question = "What is the chemical formula for water?"
context = "Water is a molecule composed of two hydrogen atoms and one oxygen atom, with the chemical formula H2O."

# Tokenize input
inputs = tokenizer.encode_plus(question, context, return_tensors="pt")

# Get model predictions
outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits

# Get the most likely beginning and end of answer with the argmax of the score
start_index = start_scores.argmax()
end_index = end_scores.argmax()

# Convert tokens to answer
tokens = inputs["input_ids"][0][start_index : end_index + 1]
answer = tokenizer.decode(tokens)

print(f"Answer: {answer}")
```

## License

This project is licensed under Apache 2.0


## Citation

If you use these models in your research or application, please cite our work:

bibtex

(PENDING)

@article{
    ...
}

## Acknowledgments

We thank the contributors of the SQuAD-v2 dataset and the developers of the Hugging Face Transformers library for providing valuable resources that made this work possible.