en-multinerd-masked-ner-more-training

pariakashani/en-multinerd-masked-ner-more-training

en-multinerd-masked-ner-more-training 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 14, 2023
Updated: Dec 14, 2023
SHA: 07096a88

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Base models

Model card

From the Hugging Face model README

en-multinerd-masked-ner-more-training

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: 0.0371
Precision: 0.9386
Recall: 0.9456
F1: 0.9421
Accuracy: 0.9920
Per-precision: 0.9960
Per-recall: 0.9961
Per-f1: 0.9961
Org-precision: 0.9364
Org-recall: 0.9419
Org-f1: 0.9392
Loc-precision: 0.9719
Loc-recall: 0.9752
Loc-f1: 0.9735
Dis-precision: 0.7059
Dis-recall: 0.7363
Dis-f1: 0.7208
Anim-precision: 0.7033
Anim-recall: 0.7275
Anim-f1: 0.7152

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: 5

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
0.0243	1.0	8205	0.0271	0.9184	0.9458	0.9319	0.9906	0.9927	0.9963	0.9945	0.8838	0.9499	0.9157	0.9586	0.9707	0.9646	0.6719	0.7274	0.6986	0.6659	0.7557	0.7080
0.0159	2.0	16410	0.0263	0.9324	0.9448	0.9386	0.9917	0.9913	0.9965	0.9939	0.9237	0.9507	0.9370	0.9613	0.9739	0.9675	0.7128	0.7233	0.7180	0.6990	0.7187	0.7087
0.0101	3.0	24615	0.0295	0.9396	0.9426	0.9411	0.9919	0.9935	0.9963	0.9949	0.9352	0.9386	0.9369	0.9704	0.9732	0.9718	0.7168	0.7286	0.7226	0.7143	0.7055	0.7098
0.0057	4.0	32820	0.0335	0.9375	0.9465	0.9420	0.9919	0.9967	0.9955	0.9961	0.9341	0.9470	0.9405	0.9694	0.9739	0.9717	0.7065	0.7499	0.7275	0.7052	0.7257	0.7153
0.004	5.0	41025	0.0371	0.9386	0.9456	0.9421	0.9920	0.9960	0.9961	0.9961	0.9364	0.9419	0.9392	0.9719	0.9752	0.9735	0.7059	0.7363	0.7208	0.7033	0.7275	0.7152

Framework versions

Transformers 4.36.1
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Dec 14, 2023
Updated: Dec 14, 2023