en-multinerd-ner-roberta

pariakashani/en-multinerd-ner-roberta

en-multinerd-ner-roberta 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 mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Dec 16, 2023
Updated: Dec 16, 2023
SHA: d11d8c1f

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Base models

Model card

From the Hugging Face model README

en-multinerd-ner-roberta

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

Loss: 0.0280
Precision: 0.9421
Recall: 0.9446
F1: 0.9434
Accuracy: 0.9903
Per-precision: 0.9917
Per-recall: 0.9970
Per-f1: 0.9943
Org-precision: 0.9766
Org-recall: 0.9837
Org-f1: 0.9801
Loc-precision: 0.9959
Loc-recall: 0.9934
Loc-f1: 0.9947
Dis-precision: 0.7665
Dis-recall: 0.7655
Dis-f1: 0.7660
Anim-precision: 0.6897
Anim-recall: 0.7039
Anim-f1: 0.6967

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: 4

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
0.038	1.0	8205	0.0452	0.8742	0.9240	0.8984	0.9830	0.9963	0.9955	0.9959	0.9616	0.9795	0.9705	0.9894	0.9944	0.9919	0.6658	0.7487	0.7048	0.6415	0.7857	0.7063
0.0294	2.0	16410	0.0410	0.9154	0.9085	0.9119	0.9856	0.9952	0.9980	0.9966	0.9697	0.9814	0.9755	0.9929	0.9946	0.9938	0.7358	0.7162	0.7259	0.7107	0.6455	0.6765
0.0202	3.0	24615	0.0429	0.9023	0.9255	0.9137	0.9854	0.9950	0.9973	0.9961	0.9791	0.9766	0.9779	0.9905	0.9958	0.9931	0.7190	0.7622	0.7400	0.7247	0.7522	0.7382
0.0149	4.0	32820	0.0455	0.9122	0.9205	0.9163	0.9858	0.9955	0.9971	0.9963	0.9795	0.9781	0.9788	0.9928	0.9954	0.9941	0.7295	0.7605	0.7447	0.7174	0.7363	0.7267

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: roberta
License: mit
Created: Dec 16, 2023
Updated: Dec 16, 2023