metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: named-entity-recognition-distilbert-A
    results: []

named-entity-recognition-distilbert-A

This model is a fine-tuned version of distilbert-base-uncased on the Multinerd dataset. It achieves the following results on the evaluation set:

Loss: 0.0606
Precision: 0.8940
Recall: 0.9027
F1: 0.8983
Accuracy: 0.9833

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.032	1.0	8205	0.0496	0.8843	0.8928	0.8885	0.9825
0.019	2.0	16410	0.0540	0.9046	0.8909	0.8977	0.9835
0.0121	3.0	24615	0.0606	0.8940	0.9027	0.8983	0.9833

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Citation

Bibtex

@software{Ali_Raza,
    author = {Raza, Ali},
    license = { BSD-2-Clause license},
    title = {{Named Entity Recognition using Multinerd}},
    url = {https://github.com/raza4729/NER}
}