---
language: en
license: mit
base_model: google-bert/bert-base-uncased
tags:
- token-classification
- bert-base-uncased
datasets:
- disham993/ElectricalNER
metrics:
- epoch: 1.0
- eval_precision: 0.8835414301929625
- eval_recall: 0.9227851102505334
- eval_f1: 0.9027369723210142
- eval_accuracy: 0.956991714467814
- eval_runtime: 2.6822
- eval_samples_per_second: 562.603
- eval_steps_per_second: 8.948
---

# disham993/electrical-ner-bert-base

## Model description

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

## Training Data

The model was trained on the disham993/ElectricalNER dataset. 

## Model Details
- **Base Model:** google-bert/bert-base-uncased
- **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: 0.8835414301929625\n- eval_recall: 0.9227851102505334\n- eval_f1: 0.9027369723210142\n- eval_accuracy: 0.956991714467814\n- eval_runtime: 2.6822\n- eval_samples_per_second: 562.603\n- eval_steps_per_second: 8.948

## Usage

```python
from transformers import AutoTokenizer, AutoModel

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