Hugfree12/my-finetuned-model

Fine-Tuned DistilBERT Text Classification Model

Model Description

This repository contains a fine-tuned DistilBERT model for binary text classification.

The model was fine-tuned using a labeled text dataset and can be used to classify new text into one of two classes.

The model was developed using the Hugging Face Transformers library and PyTorch.

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Model Details

• Model architecture: DistilBERT
• Task: Binary Text Classification
• Framework: PyTorch
• Library: Hugging Face Transformers
• Model format: Safetensors
• Number of parameters: Approximately 67 million
• Maximum sequence length: Depends on the preprocessing configuration
• Output: Two classification classes

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Intended Use

This model is intended for:

• Text classification experiments
• Academic and research purposes
• Demonstration of Transformer fine-tuning
• Natural Language Processing (NLP) experiments
• Classification of text into two predefined categories

The model should be used only within the context of the dataset and task on which it was trained.

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Not Intended For

This model should not be used:

• As a replacement for professional judgment
• For high-stakes automated decisions without human review
• Outside the domain represented by the training data without additional validation
• As a general-purpose language model
• As evidence that the model will perform equally well on unseen populations or different datasets

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Training

Base Model

The model is based on the DistilBERT Transformer architecture.

DistilBERT is a smaller and faster version of BERT designed to retain much of BERT's language understanding capability while reducing computational requirements.

The pretrained model was further trained (fine-tuned) on a labeled text classification dataset.

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Fine-Tuning Process

The general workflow used for this project was:

1. Load the text dataset.
2. Prepare the text and labels.
3. Split the data into training, validation, and test sets.
4. Tokenize the text using the DistilBERT tokenizer.
5. Load a pretrained DistilBERT model for sequence classification.
6. Fine-tune the model using the training dataset.
7. Evaluate the model using the validation dataset during the training process.
8. Evaluate the final model on the held-out test dataset.
9. Calculate classification metrics.
10. Generate a confusion matrix.
11. Save the trained model and tokenizer.
12. Upload the fine-tuned model to Hugging Face.
13. Load the uploaded model again from Hugging Face.
14. Test the loaded model on new text.

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Dataset

The model was trained using a labeled text classification dataset.

The dataset contains:

• A text input
• A corresponding binary class label

The exact dataset source and dataset-specific description should be added here if required by the project.

Dataset Split

The data was divided into:

• Training set
• Validation set
• Test set

The final test set contained:

200 samples

with the following class distribution:

Class	Number of Samples
Class 0	100
Class 1	100
Total	200

The test set was therefore balanced between the two classes.

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Labels

The model performs binary classification.

The output classes are:

Label	Meaning
0	Class 0
1	Class 1

Note: Replace "Class 0" and "Class 1" with the actual meaning of your labels if your project defines them differently.

For example, if your dataset defines:

0 = Negative
1 = Positive