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---
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
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