LegalBert_NoDuplicates_20Partition_5000WordsFrequency

Kamer/LegalBert_NoDuplicates_20Partition_5000WordsFrequency

LegalBert_NoDuplicates_20Partition_5000WordsFrequency is a text classification model from Kamer. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-sa-4.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: cc-by-sa-4.0
Model type: bert
Access: Public
Created: Sep 3, 2023
Updated: Sep 3, 2023
SHA: 8e5199a1

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Base models

nlpaueb/legal-bert-base-uncased

Model card

From the Hugging Face model README

LegalBert_NoDuplicates_20Partition_5000WordsFrequency

This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4410
Accuracy: 0.9049
F1 Macro: 0.8387
F1 Class 0: 0.9350
F1 Class 1: 0.6
F1 Class 2: 0.9290
F1 Class 3: 0.8000
F1 Class 4: 0.9014
F1 Class 5: 0.9388
F1 Class 6: 0.8119
F1 Class 7: 0.9317
F1 Class 8: 0.9804
F1 Class 9: 0.8595
F1 Class 10: 0.8834
F1 Class 11: 0.6087
F1 Class 12: 0.8280
F1 Class 13: 0.8333
F1 Class 14: 0.8808
F1 Class 15: 0.5588
F1 Class 16: 0.7273
F1 Class 17: 0.9799
F1 Class 18: 0.8440
F1 Class 19: 0.9412

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	Accuracy	F1 Macro	F1 Class 0	F1 Class 1	F1 Class 2	F1 Class 3	F1 Class 4	F1 Class 5	F1 Class 6	F1 Class 7	F1 Class 8	F1 Class 9	F1 Class 10	F1 Class 11	F1 Class 12	F1 Class 13	F1 Class 14	F1 Class 15	F1 Class 16	F1 Class 17	F1 Class 18	F1 Class 19
1.4309	0.44	250	0.9084	0.7836	0.5020	0.8895	0.0	0.8333	0.6111	0.5205	0.6966	0.4928	0.7551	0.8496	0.7397	0.8260	0.0	0.6420	0.0	0.5325	0.0	0.0	0.9700	0.6813	0.0
0.6988	0.88	500	0.5860	0.8602	0.6434	0.9078	0.0	0.8871	0.6829	0.7907	0.7835	0.7579	0.9091	0.9293	0.8421	0.8542	0.0	0.7524	0.3448	0.8243	0.0	0.0	0.9736	0.8000	0.8276
0.4866	1.33	750	0.5249	0.8765	0.7060	0.9189	0.0	0.8866	0.8511	0.8095	0.8269	0.8387	0.9375	0.9412	0.7883	0.8801	0.1538	0.7870	0.8085	0.8203	0.0976	0.0	0.9690	0.8348	0.9697
0.4198	1.77	1000	0.4760	0.8796	0.7172	0.9177	0.0	0.9137	0.8000	0.8406	0.7957	0.7957	0.9308	0.9524	0.7612	0.8718	0.2857	0.8012	0.8085	0.8344	0.2800	0.0	0.9799	0.8333	0.9412
0.3275	2.21	1250	0.4650	0.8867	0.7405	0.9221	0.0	0.9201	0.8000	0.8608	0.8421	0.8043	0.9367	0.9259	0.8372	0.8731	0.3478	0.8121	0.8085	0.8407	0.5312	0.0	0.9737	0.8333	0.9412
0.2874	2.65	1500	0.4662	0.8916	0.7792	0.9221	0.6	0.9160	0.8000	0.7879	0.8224	0.7959	0.9325	0.9804	0.8430	0.8896	0.5	0.7862	0.8085	0.8693	0.5634	0.0	0.9829	0.8421	0.9412
0.2563	3.1	1750	0.4427	0.8978	0.7627	0.9310	0.25	0.9272	0.8000	0.88	0.8515	0.8602	0.9383	0.9615	0.8438	0.8896	0.3333	0.8182	0.8085	0.8542	0.5574	0.0	0.9784	0.8302	0.9412
0.2206	3.54	2000	0.4378	0.8996	0.7920	0.9298	0.6	0.9251	0.8000	0.9067	0.8468	0.8200	0.9317	0.9804	0.8413	0.8913	0.5217	0.8208	0.8333	0.8571	0.5574	0.0	0.9829	0.8519	0.9412
0.1966	3.98	2250	0.4262	0.9031	0.8378	0.9361	0.6	0.9247	0.8000	0.9067	0.9143	0.8235	0.9317	0.9709	0.8739	0.8820	0.5714	0.8239	0.8571	0.8658	0.5846	0.7273	0.9784	0.8421	0.9412
0.1565	4.42	2500	0.4355	0.9075	0.8394	0.9390	0.6	0.9307	0.8000	0.9315	0.9184	0.8235	0.9317	0.9703	0.8889	0.8872	0.5833	0.8317	0.8085	0.8725	0.5758	0.7273	0.9829	0.8440	0.9412
0.149	4.87	2750	0.4410	0.9049	0.8387	0.9350	0.6	0.9290	0.8000	0.9014	0.9388	0.8119	0.9317	0.9804	0.8595	0.8834	0.6087	0.8280	0.8333	0.8808	0.5588	0.7273	0.9799	0.8440	0.9412

Framework versions

Transformers 4.32.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: cc-by-sa-4.0
Created: Sep 3, 2023
Updated: Sep 3, 2023