Model Card for DistilBERT Text Classification

This is a DistilBERT model fine-tuned for text classification tasks.

Model Details

Model Description

This DistilBERT model is fine-tuned for text classification tasks. It is designed to classify texts into different categories based on the provided dataset.

Developed by: Thiago Adriano
Model type: DistilBERT for Sequence Classification
Language(s) (NLP): Portuguese
License: MIT License
Finetuned from model: distilbert-base-uncased

Model Sources

Repository: Link to your repository

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("tadrianonet/distilbert-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("tadrianonet/distilbert-text-classification")

inputs = tokenizer("Sample text for classification", return_tensors="pt")
outputs = model(**inputs)

Training Details

Training Data

The training data consists of text-label pairs in Portuguese. The data is preprocessed to tokenize the text and convert labels to numerical format.

Training Procedure

The model is fine-tuned using the Hugging Face Trainer API with the following hyperparameters:

Training regime: fp32
Learning rate: 2e-5
Batch size: 16
Epochs: 3

Speeds, Sizes, Times

Training time: Approximately 10 minutes on a single GPU

Evaluation

Testing Data, Factors & Metrics

Testing Data

The testing data is a separate set of text-label pairs used to evaluate the model's performance.

Factors

The evaluation is disaggregated by accuracy and loss.

Metrics

Accuracy: Measures the proportion of correct predictions
Loss: Measures the error in the model's predictions

Results

Evaluation Results:
- Loss: 0.692
- Accuracy: 50%

Summary

The model achieves 50% accuracy on the evaluation dataset, indicating that further fine-tuning and evaluation on a more diverse dataset may be necessary.

Model Examination

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: GPU
Hours used: 0.2 hours
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications

Model Architecture and Objective

The model is based on DistilBERT, a smaller, faster, and cheaper version of BERT, designed for efficient text classification.

Compute Infrastructure

Hardware

Hardware Type: Single GPU
GPU Model: [More Information Needed]

Software

Framework: Transformers 4.x
Library: PyTorch

Citation

BibTeX:

1 bibtex @misc{thiago_adriano_2024_distilbert, author = {Thiago Adriano}, title = {DistilBERT Text Classification}, year = {2024}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/tadrianonet/distilbert-text-classification}}, } 1

APA:

Thiago Adriano. (2024). DistilBERT Text Classification. Hugging Face. https://huggingface.co/tadrianonet/distilbert-text-classification

More Information

For more details, visit the Hugging Face model page.

Model Card Authors

Thiago Adriano

Model Card Contact

For more information, contact Thiago Adriano at [tadriano.dev@gmail.com]