Llama-3.2-1B-subjectivity-english

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6766
• Macro F1: 0.7718
• Macro P: 0.7731
• Macro R: 0.7715
• Subj F1: 0.7862
• Subj P: 0.7689
• Subj R: 0.8042
• Accuracy: 0.7727

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: 0.0001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use 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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Macro F1	Macro P	Macro R	Subj F1	Subj P	Subj R	Accuracy
No log	1.0	52	0.6133	0.6564	0.6716	0.6595	0.7092	0.6451	0.7875	0.6645
No log	2.0	104	0.5806	0.7458	0.7469	0.7454	0.7617	0.7450	0.7792	0.7468
No log	3.0	156	0.5663	0.7464	0.7523	0.7462	0.7717	0.7313	0.8167	0.7489
No log	4.0	208	0.5916	0.7568	0.7650	0.7566	0.7836	0.7363	0.8375	0.7597
No log	5.0	260	0.6430	0.7693	0.7716	0.7689	0.7863	0.7617	0.8125	0.7706
No log	6.0	312	0.6766	0.7718	0.7731	0.7715	0.7862	0.7689	0.8042	0.7727

Framework versions

• PEFT 0.14.0
• Transformers 4.49.0
• Pytorch 2.5.1+cu121
• Datasets 3.3.1
• Tokenizers 0.21.0