early_fusion_sentiment_True_hate_speech_True_extra_layer_False

iTroned/early_fusion_sentiment_True_hate_speech_True_extra_layer_False

early_fusion_sentiment_True_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: 62af8bde

Model card

From the Hugging Face model README

early_fusion_sentiment_True_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: 0.6261
Accuracy Offensive: 0.7451
F1 Offensive: 0.7217
Accuracy Targeted: 0.7304
F1 Targeted: 0.7065
Accuracy Stance: 0.7281
F1 Stance: 0.6717

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
0.7784	1.0	1324	0.7499	0.6545	0.5178	0.6545	0.5178	0.7009	0.5777
0.7235	2.0	2648	0.7152	0.6760	0.5810	0.6567	0.5239	0.7009	0.5777
0.7038	3.0	3972	0.6886	0.7002	0.6385	0.6903	0.6273	0.7009	0.5777
0.6895	4.0	5296	0.6824	0.6971	0.6265	0.6945	0.6304	0.7028	0.5846
0.6831	5.0	6620	0.6631	0.7258	0.6965	0.7153	0.6910	0.7115	0.6358
0.6725	6.0	7944	0.6569	0.7190	0.6748	0.7119	0.6763	0.7130	0.6371
0.661	7.0	9268	0.6523	0.7387	0.7178	0.7228	0.7053	0.7107	0.6576
0.6655	8.0	10592	0.6447	0.7315	0.6967	0.7224	0.6952	0.7168	0.6531
0.6577	9.0	11916	0.6460	0.7213	0.6758	0.7217	0.6862	0.7187	0.6526
0.6458	10.0	13240	0.6378	0.7417	0.7201	0.7330	0.7150	0.7209	0.6708
0.6393	11.0	14564	0.6333	0.7432	0.7195	0.7345	0.7125	0.7232	0.6679
0.6527	12.0	15888	0.6336	0.7375	0.7082	0.7281	0.6998	0.7236	0.6620
0.6317	13.0	17212	0.6314	0.7515	0.7361	0.7341	0.7177	0.7205	0.6729
0.6292	14.0	18536	0.6261	0.7451	0.7217	0.7304	0.7065	0.7281	0.6717
0.6301	15.0	19860	0.6282	0.7617	0.7535	0.7258	0.7123	0.7179	0.6774
0.6174	16.0	21184	0.6308	0.7383	0.7022	0.7311	0.6975	0.7292	0.6691

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