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MHGanainy/
roberta-base-legal-multi-downstream-ildc

Name: roberta-base-legal-multi-downstream-ildc
Availability: InStock

generated_from_trainer

MHGanainy/roberta-base-legal-multi

finetune

autotrain_compatible

endpoints_compatible

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roberta-base-legal-multi-downstream-ildc

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

Loss: 0.7550
Accuracy: 0.5111
Precision: 0.5066
Recall: 0.8491
F1: 0.6346
Best Threshold: 0.3289

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 1
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Best Threshold
0.6846	1.0	1010	0.7224	0.5010	0.5005	0.9920	0.6653	0.3777
0.6809	2.0	2020	0.7399	0.5020	0.55	0.0221	0.0426	0.3502
0.6824	3.0	3030	0.7805	0.5040	0.5021	0.9437	0.6555	0.2994
0.6804	4.0	4040	0.7550	0.5111	0.5066	0.8491	0.6346	0.3289

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

MHGanainy/roberta-base-legal-multi-downstream-ildc

roberta-base-legal-multi-downstream-ildc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MHGanainy/
roberta-base-legal-multi-downstream-ildc