README.md

metadata

language: en
license: mit
base_model: distilbert/distilbert-base-uncased
tags:
  - text-classification
  - distilbert-base-uncased
datasets:
  - disham993/ElectricalDeviceFeedbackBalanced
metrics:
  - epoch: 1
  - eval_f1: 0.8353275880967258
  - eval_accuracy: 0.856508875739645
  - eval_runtime: 0.4632
  - eval_samples_per_second: 2918.69
  - eval_steps_per_second: 47.493

disham993/electrical-classification-distilbert-base-uncased

Model description

This model is fine-tuned from distilbert/distilbert-base-uncased for text-classification tasks.

Training Data

The model was trained on the disham993/ElectricalDeviceFeedbackBalanced dataset.

Model Details

• Base Model: distilbert/distilbert-base-uncased
• Task: text-classification
• Language: en
• Dataset: disham993/ElectricalDeviceFeedbackBalanced

Training procedure

Training hyperparameters

[Please add your training hyperparameters here]

Evaluation results

Metrics\n- epoch: 1.0\n- eval_f1: 0.8353275880967258\n- eval_accuracy: 0.856508875739645\n- eval_runtime: 0.4632\n- eval_samples_per_second: 2918.69\n- eval_steps_per_second: 47.493

Usage

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-distilbert-base-uncased")
model = AutoModel.from_pretrained("disham993/electrical-classification-distilbert-base-uncased")

Limitations and bias

[Add any known limitations or biases of the model]

Training Infrastructure

[Add details about training infrastructure used]

Last update

2025-01-05