metadata

language: en
license: mit
base_model: distilbert/distilbert-base-uncased
tags:
  - token-classification
  - distilbert-base-uncased
datasets:
  - disham993/ElectricalNER
metrics:
  - epoch: 1
  - 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 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

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