README.md

metadata

language: en
license: mit
base_model: answerdotai/ModernBERT-base
tags:
  - text-classification
  - ModernBERT-base
datasets:
  - disham993/ElectricalDeviceFeedbackBalanced
metrics:
  - epoch: 1
  - eval_f1: 0.806771655637414
  - eval_accuracy: 0.8269230769230769
  - eval_runtime: 1.6141
  - eval_samples_per_second: 837.642
  - eval_steps_per_second: 13.63

disham993/electrical-classification-ModernBERT-base

Model description

This model is fine-tuned from answerdotai/ModernBERT-base for text-classification tasks.

Training Data

The model was trained on the disham993/ElectricalDeviceFeedbackBalanced dataset.

Model Details

• Base Model: answerdotai/ModernBERT-base
• 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: 0.806771655637414\n- eval_accuracy: 0.8269230769230769\n- eval_runtime: 1.6141\n- eval_samples_per_second: 837.642\n- eval_steps_per_second: 13.63

Usage

from transformers import AutoTokenizer, AutoModel

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

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