ro001 Open-source Text Classification Model - Free, Practical, and Precise for All Kinds of Text Classification Tasks

Ro001

Developed by jiyometrik

A text classification model fine-tuned based on distilbert-base-uncased, achieving an F1 score of 0.6147

Large Language Model

Transformers

Open Source License:Apache-2.0 #Lightweight BERT #Text Classification #Efficient Fine-tuning

Downloads 23

Release Time : 3/20/2025

Model Overview

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset, primarily used for text classification tasks.

Model Features

Lightweight Model

Based on the DistilBERT architecture, it is more lightweight than the original BERT model and offers faster inference speed.

Efficient Fine-tuning

Achieved an F1 score of 0.6147 after 4 training epochs.

Model Capabilities

Text Classification

Natural Language Processing

Use Cases

Text Analysis

Sentiment Analysis

Can be used to analyze the sentiment tendency of text.

Topic Classification

Can be used to classify text into predefined topic categories.

🚀 ro001

This is a fine - tuned model based on DistilBERT, which can achieve good performance on specific tasks.

🚀 Quick Start

This model, named ro001, is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased) on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2832
F1: 0.6147

📦 Installation

Since this model is based on the transformers library, you can install it using the following command:

pip install transformers

📚 Documentation

Model description

This model is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased). However, more information about its specific application scenarios and optimization directions is yet to be provided.

Intended uses & limitations

More information about the intended uses and limitations of this model is needed.

Training and evaluation data

Details about the training and evaluation data are yet to be provided.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP