---
language: en
license: mit
base_model: answerdotai/ModernBERT-large
tags:
- token-classification
- ModernBERT-large
datasets:
- disham993/ElectricalNER
metrics:
- epoch: 1.0
- eval_precision: 0.9170362009191324
- eval_recall: 0.917258064516129
- eval_f1: 0.9171471193000846
- eval_accuracy: 0.965673339950132
- eval_runtime: 3.7584
- eval_samples_per_second: 401.504
- eval_steps_per_second: 6.386
---

# disham993/electrical-ner-modernbert-large

## Model description

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

## Training Data

The model was trained on the disham993/ElectricalNER dataset. 

## Model Details
- **Base Model:** answerdotai/ModernBERT-large
- **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: 0.9170362009191324\n- eval_recall: 0.917258064516129\n- eval_f1: 0.9171471193000846\n- eval_accuracy: 0.965673339950132\n- eval_runtime: 3.7584\n- eval_samples_per_second: 401.504\n- eval_steps_per_second: 6.386

## Usage

```python
from transformers import AutoTokenizer, AutoModel

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

## 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