roberta-base_legal_ner_finetuned

khalidrajan/roberta-base_legal_ner_finetuned

roberta-base_legal_ner_finetuned is a machine learning model from khalidrajan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

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At a glance

License: mit
Model type: roberta
Access: Public
Created: Aug 28, 2024
Updated: Nov 6, 2024
SHA: 34e95984

Base models

FacebookAI/roberta-base

Model card

From the Hugging Face model README

roberta-base_legal_ner_finetuned

This model is a fine-tuned version of FacebookAI/roberta-base on the Darrow LegalLens Shared Task NER dataset. It achieves the following results on the evaluation set:

Loss: 0.2416
Law Precision: 0.8319
Law Recall: 0.8785
Law F1: 0.8545
Law Number: 107
Violated by Precision: 0.8361
Violated by Recall: 0.7183
Violated by F1: 0.7727
Violated by Number: 71
Violated on Precision: 0.5
Violated on Recall: 0.5
Violated on F1: 0.5
Violated on Number: 64
Violation Precision: 0.6494
Violation Recall: 0.7032
Violation F1: 0.6752
Violation Number: 374
Overall Precision: 0.6843
Overall Recall: 0.7143
Overall F1: 0.6990
Overall Accuracy: 0.9553

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Law Precision	Law Recall	Law F1	Law Number	Violated by Precision	Violated by Recall	Violated by F1	Violated by Number	Violated on Precision	Violated on Recall	Violated on F1	Violated on Number	Violation Precision	Violation Recall	Violation F1	Violation Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	85	0.7386	0.0	0.0	0.0	107	0.0	0.0	0.0	71	0.0	0.0	0.0	64	0.0	0.0	0.0	374	0.0	0.0	0.0	0.7707
No log	2.0	170	0.3510	0.0	0.0	0.0	107	0.0	0.0	0.0	71	0.0	0.0	0.0	64	0.2072	0.2781	0.2374	374	0.2072	0.1688	0.1860	0.8901
No log	3.0	255	0.2471	0.4265	0.2710	0.3314	107	0.0	0.0	0.0	71	0.3810	0.125	0.1882	64	0.3965	0.4813	0.4348	374	0.3996	0.3523	0.3745	0.9199
No log	4.0	340	0.1996	0.7596	0.7383	0.7488	107	0.5128	0.5634	0.5369	71	0.3827	0.4844	0.4276	64	0.5101	0.6096	0.5554	374	0.5324	0.6136	0.5701	0.9385
No log	5.0	425	0.1984	0.7946	0.8318	0.8128	107	0.64	0.6761	0.6575	71	0.5091	0.4375	0.4706	64	0.5102	0.6684	0.5787	374	0.5669	0.6737	0.6157	0.9449
0.5018	6.0	510	0.2447	0.7456	0.7944	0.7692	107	0.75	0.6761	0.7111	71	0.4068	0.375	0.3902	64	0.6110	0.6845	0.6456	374	0.6296	0.6705	0.6494	0.9465
0.5018	7.0	595	0.2264	0.8125	0.8505	0.8311	107	0.7736	0.5775	0.6613	71	0.4754	0.4531	0.4640	64	0.6276	0.7166	0.6692	374	0.6570	0.6964	0.6761	0.9511
0.5018	8.0	680	0.2243	0.8598	0.8598	0.8598	107	0.7812	0.7042	0.7407	71	0.4912	0.4375	0.4628	64	0.6209	0.7139	0.6642	374	0.6641	0.7094	0.6860	0.9541
0.5018	9.0	765	0.2327	0.7934	0.8972	0.8421	107	0.7808	0.8028	0.7917	71	0.4231	0.5156	0.4648	64	0.6037	0.7005	0.6485	374	0.6346	0.7273	0.6778	0.9547
0.5018	10.0	850	0.2416	0.8319	0.8785	0.8545	107	0.8361	0.7183	0.7727	71	0.5	0.5	0.5	64	0.6494	0.7032	0.6752	374	0.6843	0.7143	0.6990	0.9553

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Type: roberta
License: mit
Created: Aug 28, 2024
Updated: Nov 6, 2024