This model is a fine-tuned version of the bert-base-uncased transformer model, specifically tailored for multi-class text classification. It was developed as part of the Frugal AI Challenge to classify text into eight distinct categories. The model incorporates a custom classification head and leverages class weighting to address dataset imbalance.

Dataset

Source: Frugal AI Challenge Text Task Dataset
Classes: 8 unique labels representing various categories of text
Preprocessing: Tokenization using BertTokenizer with padding and truncation to a maximum sequence length of 128.

Model Architecture

Base Model: bert-base-uncased
Classification Head: Custom head with weighted cross-entropy loss to handle class imbalance.
Number of Labels: 8

Training Details

Optimizer: AdamW
Learning Rate: 2e-5
Batch Size: 16 (for both training and evaluation)
Epochs: 3
Weight Decay: 0.01
Evaluation Strategy: Performed at the end of each epoch
Hardware: Trained on GPUs for efficient computation

Performance Metrics (Validation Set)

The model achieved the following performance metrics on the validation set:

Class	Precision	Recall	F1-Score	Support
5_science_unreliable	0.65	0.71	0.68	160
1_not_happening	0.69	0.80	0.74	148
4_solutions_harmful_unnecessary	0.62	0.72	0.66	155
0_not_relevant	0.82	0.60	0.70	324
6_proponents_biased	0.62	0.64	0.63	157
7_fossil_fuels_needed	0.59	0.68	0.63	57
2_not_human	0.68	0.70	0.69	141
3_not_bad	0.59	0.62	0.61	77

Overall Accuracy: 68%
Macro Average: Precision: 0.66, Recall: 0.68, F1-Score: 0.67
Weighted Average: Precision: 0.69, Recall: 0.68, F1-Score: 0.68

Training Evolution

Training and Validation Loss

The training and validation loss evolution over epochs is shown below:

Validation Accuracy

The validation accuracy evolution over epochs is shown below:

Confusion Matrix

The confusion matrix below illustrates the model's performance on the validation set, highlighting areas of strength and potential misclassifications:

Key Features

Class Weighting: Addressed dataset imbalance by incorporating class weights during training.
Custom Loss Function: Used weighted cross-entropy loss for better handling of underrepresented classes.
Evaluation Metrics: Accuracy, precision, recall, and F1-score were computed to provide a comprehensive understanding of the model's performance.

Usage

This model can be used for multi-class text classification tasks where the input text needs to be categorized into one of the eight predefined classes. It is particularly suited for datasets with class imbalance, thanks to its weighted loss function.

Example Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the fine-tuned model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("ParisNeo/bert-frugal-ai-text-classification")
tokenizer = AutoTokenizer.from_pretrained("ParisNeo/bert-frugal-ai-text-classification")

# Tokenize input text
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)

# Perform inference
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

print(f"Predicted Class: {predicted_class}")

Limitations

Dataset-Specific: The model's performance is optimized for the Frugal AI Challenge dataset and may require further fine-tuning for other datasets.
Class Imbalance: While class weighting mitigates imbalance, some underrepresented classes may still have lower performance.
Sequence Length: Input text is truncated to a maximum length of 128 tokens, which may result in loss of information for longer texts.

Citation

If you use this model in your research or application, please cite it as:

@model{ParisNeo_bert_frugal_ai_text_classification,
  author = {ParisNeo},
  title = {BERT Fine-Tuned for Frugal AI Challenge - Text Task},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/ParisNeo/bert-frugal-ai-text-classification}
}

Acknowledgments

Special thanks to the Frugal AI Challenge organizers for providing the dataset and fostering innovation in AI research.