metadata

language: en
license: mit
base_model: answerdotai/ModernBERT-large
tags:
  - token-classification
  - ModernBERT-large
datasets:
  - disham993/ElectricalNER
metrics:
  - epoch: 1
  - 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 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

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