metadata

language: en
license: mit
base_model: answerdotai/ModernBERT-base
tags:
  - token-classification
  - ModernBERT-base
datasets:
  - disham993/ElectricalNER
metrics:
  - epoch: 1
  - eval_precision: 0.8935291782453354
  - eval_recall: 0.9075806451612904
  - eval_f1: 0.9005001000200039
  - eval_accuracy: 0.9586046624222324
  - eval_runtime: 2.509
  - eval_samples_per_second: 601.44
  - eval_steps_per_second: 9.566

disham993/electrical-ner-modernbert-base

Model description

This model is fine-tuned from answerdotai/ModernBERT-base for token-classification tasks.

Training Data

The model was trained on the disham993/ElectricalNER dataset.

Model Details

Base Model: answerdotai/ModernBERT-base
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: 0.8935291782453354\n- eval_recall: 0.9075806451612904\n- eval_f1: 0.9005001000200039\n- eval_accuracy: 0.9586046624222324\n- eval_runtime: 2.509\n- eval_samples_per_second: 601.44\n- eval_steps_per_second: 9.566

Usage

from transformers import AutoTokenizer, AutoModel

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