---
language: en
license: mit
base_model: answerdotai/ModernBERT-base
tags:
- token-classification
- ModernBERT-base
datasets:
- disham993/ElectricalNER
metrics:
- epoch: 1.0
- eval_precision: 0.8935291782453354
- eval_recall: 0.9075806451612904
- eval_f1: 0.9005001000200039
- eval_accuracy: 0.9586046624222324
- eval_runtime: 2.509
- eval_samples_per_second: 601.44
- eval_steps_per_second: 9.566
---

# disham993/electrical-ner-modernbert-base

## Model description

This model is fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) for token-classification tasks.

## Training Data

The model was trained on the disham993/ElectricalNER dataset. 

## Model Details
- **Base Model:** answerdotai/ModernBERT-base
- **Task:** token-classification
- **Language:** en
- **Dataset:** disham993/ElectricalNER

## Training procedure

### Training hyperparameters
[Please add your training hyperparameters here]

## Evaluation results

### Metrics\n- epoch: 1.0\n- eval_precision: 0.8935291782453354\n- eval_recall: 0.9075806451612904\n- eval_f1: 0.9005001000200039\n- eval_accuracy: 0.9586046624222324\n- eval_runtime: 2.509\n- eval_samples_per_second: 601.44\n- eval_steps_per_second: 9.566

## Usage

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-ner-modernbert-base")
model = AutoModel.from_pretrained("disham993/electrical-ner-modernbert-base")
```

## Limitations and bias

[Add any known limitations or biases of the model]

## Training Infrastructure

[Add details about training infrastructure used]

## Last update

2024-12-30