iw-conspir-v3

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

• Loss: 0.3976
• F1 Contradictory: 0.0
• F1 Overriding Suspicion: 0.7035
• F1 Nefarious Intent: 0.6841
• F1 Persecuted Victim: 0.2857
• F1 Immune To Evidence: 0.3396
• F1 Reinterpreting Randomness: 0.4010
• Macro F1: 0.4023
• Micro F1: 0.5893

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: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Contradictory	F1 Overriding Suspicion	F1 Nefarious Intent	F1 Persecuted Victim	F1 Immune To Evidence	F1 Reinterpreting Randomness	Macro F1	Micro F1
0.2721	1.0	100	0.3105	0.0	0.6	0.5649	0.2092	0.28	0.3920	0.3410	0.4886
0.2332	2.0	200	0.3372	0.0177	0.6601	0.6708	0.3167	0.3344	0.3814	0.3968	0.5335
0.1490	3.0	300	0.3975	0.0	0.7035	0.6841	0.2857	0.3396	0.4010	0.4023	0.5893
0.1420	4.0	400	0.4999	0.0	0.7089	0.6701	0.2532	0.3314	0.3830	0.3911	0.5987
0.1038	5.0	500	0.5314	0.0	0.6874	0.6601	0.2532	0.3541	0.4070	0.3936	0.5871
0.0963	6.0	600	0.5686	0.0	0.6617	0.6628	0.2133	0.3263	0.3878	0.3753	0.5741

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2