---
language: en
license: mit
base_model: distilbert/distilbert-base-uncased
tags:
- text-classification
- distilbert-base-uncased
datasets:
- disham993/ElectricalDeviceFeedbackBalanced
metrics:
- epoch: 1.0
- 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](https://huggingface.co/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

```python
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