---
language: en
license: mit
base_model: distilbert/distilbert-base-uncased
tags:
- token-classification
- distilbert-base-uncased
datasets:
- disham993/ElectricalNER
metrics:
- epoch: 1.0
- eval_precision: 0.8758209405903223
- eval_recall: 0.9169366948549752
- eval_f1: 0.8959073359073358
- eval_accuracy: 0.9532186105799872
- eval_runtime: 1.7541
- eval_samples_per_second: 860.279
- eval_steps_per_second: 13.682
---

# disham993/electrical-ner-distilbert-base

## Model description

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

## Training Data

The model was trained on the disham993/ElectricalNER dataset. 

## Model Details
- **Base Model:** distilbert/distilbert-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: 0.8758209405903223\n- eval_recall: 0.9169366948549752\n- eval_f1: 0.8959073359073358\n- eval_accuracy: 0.9532186105799872\n- eval_runtime: 1.7541\n- eval_samples_per_second: 860.279\n- eval_steps_per_second: 13.682

## Usage

```python
from transformers import AutoTokenizer, AutoModel

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