en-multinerd-ner-upsampled

pariakashani/en-multinerd-ner-upsampled

en-multinerd-ner-upsampled is a token classification model from pariakashani. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Dec 15, 2023
Updated: Dec 15, 2023
SHA: 53b48ea1

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Base models

Model card

From the Hugging Face model README

en-multinerd-ner-upsampled

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

Loss: 0.0339
Precision: 0.9370
Recall: 0.9459
F1: 0.9414
Accuracy: 0.9919
Per-precision: 0.9951
Per-recall: 0.9967
Per-f1: 0.9959
Org-precision: 0.9359
Org-recall: 0.9445
Org-f1: 0.9402
Loc-precision: 0.9708
Loc-recall: 0.9752
Loc-f1: 0.9730
Dis-precision: 0.7131
Dis-recall: 0.7404
Dis-f1: 0.7265
Anim-precision: 0.6809
Anim-recall: 0.7187
Anim-f1: 0.6993

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: 16
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	Per-precision	Per-recall	Per-f1	Org-precision	Org-recall	Org-f1	Loc-precision	Loc-recall	Loc-f1	Dis-precision	Dis-recall	Dis-f1	Anim-precision	Anim-recall	Anim-f1
0.0293	1.0	9223	0.0262	0.9302	0.9363	0.9333	0.9911	0.9928	0.9951	0.9939	0.9256	0.9357	0.9306	0.9673	0.9622	0.9647	0.6819	0.7286	0.7045	0.6710	0.6799	0.6754
0.0149	2.0	18446	0.0282	0.9353	0.9466	0.9409	0.9918	0.9948	0.9969	0.9959	0.9322	0.9448	0.9385	0.9687	0.9725	0.9706	0.7190	0.7457	0.7321	0.675	0.7381	0.7051
0.0084	3.0	27669	0.0339	0.9370	0.9459	0.9414	0.9919	0.9951	0.9967	0.9959	0.9359	0.9445	0.9402	0.9708	0.9752	0.9730	0.7131	0.7404	0.7265	0.6809	0.7187	0.6993

Framework versions

Transformers 4.36.1
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Dec 15, 2023
Updated: Dec 15, 2023