early_fusion_sentiment_False_hate_speech_True_extra_layer_False

iTroned/early_fusion_sentiment_False_hate_speech_True_extra_layer_False

early_fusion_sentiment_False_hate_speech_True_extra_layer_False is a machine learning model from iTroned. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

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

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Public

Updated Apr 17, 2025

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30.8 MB on disk

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At a glance

Library: transformers
Access: Public
Created: Apr 17, 2025
Updated: Apr 17, 2025
SHA: 34b4feef

Model card

From the Hugging Face model README

early_fusion_sentiment_False_hate_speech_True_extra_layer_False

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

Loss: 0.6349
Accuracy Offensive: 0.7360
F1 Offensive: 0.7105
Accuracy Targeted: 0.7338
F1 Targeted: 0.7081
Accuracy Stance: 0.7247
F1 Stance: 0.6695

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Offensive	F1 Offensive	Accuracy Targeted	F1 Targeted	Accuracy Stance	F1 Stance
0.7745	1.0	1324	0.7518	0.6545	0.5178	0.6545	0.5178	0.7009	0.5777
0.7283	2.0	2648	0.7200	0.6707	0.5609	0.6545	0.5178	0.7009	0.5777
0.7107	3.0	3972	0.6968	0.6956	0.6235	0.6881	0.6076	0.7009	0.5777
0.6931	4.0	5296	0.6902	0.6873	0.6022	0.6903	0.6196	0.7009	0.5778
0.6858	5.0	6620	0.6712	0.7115	0.6677	0.7066	0.6748	0.7096	0.6188
0.6793	6.0	7944	0.6731	0.7020	0.6346	0.7051	0.6531	0.7096	0.6133
0.6689	7.0	9268	0.6595	0.7307	0.7057	0.7183	0.6986	0.7085	0.6514
0.6681	8.0	10592	0.6605	0.7141	0.6597	0.7141	0.6719	0.7145	0.6324
0.658	9.0	11916	0.6548	0.7156	0.6645	0.7190	0.6793	0.7156	0.6364
0.6537	10.0	13240	0.6463	0.7368	0.7130	0.7258	0.7062	0.7183	0.6673
0.643	11.0	14564	0.6417	0.7398	0.7141	0.7304	0.7070	0.7205	0.6644
0.6512	12.0	15888	0.6397	0.7323	0.6974	0.7323	0.7040	0.7209	0.6572
0.6369	13.0	17212	0.6385	0.7553	0.7421	0.7273	0.7114	0.7134	0.6711
0.6354	14.0	18536	0.6349	0.7360	0.7105	0.7338	0.7081	0.7247	0.6695
0.6334	15.0	19860	0.6357	0.7557	0.7447	0.7270	0.7116	0.7156	0.6751
0.6215	16.0	21184	0.6375	0.7364	0.7039	0.7300	0.6972	0.7232	0.6583

Framework versions

Transformers 4.50.2
Pytorch 2.6.0+cu124
Datasets 3.0.1
Tokenizers 0.21.1

Library: transformers
Created: Apr 17, 2025
Updated: Apr 17, 2025