MTL-amroberta-finetuned

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

• Loss: 1.6274
• Stance F1: 0.7381
• Sentiment F1: 0.6804
• F1: 0.7092
• Stance Acc: 0.7269
• Sentiment Acc: 0.6870

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: 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: cosine
• lr_scheduler_warmup_steps: 300
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Stance F1	Sentiment F1	F1	Stance Acc	Sentiment Acc
1.7405	1.0	377	1.5961	0.6525	0.6702	0.6613	0.6446	0.6771
1.3386	2.0	754	1.4527	0.7023	0.6706	0.6864	0.6933	0.6820
1.1046	3.0	1131	1.4365	0.7172	0.6804	0.6988	0.7070	0.6883
0.8845	4.0	1508	1.4615	0.7259	0.6901	0.7080	0.7157	0.6958
0.8033	5.0	1885	1.5284	0.7425	0.6823	0.7124	0.7344	0.6883
0.6945	6.0	2262	1.5812	0.7405	0.6805	0.7105	0.7319	0.6870
0.5849	7.0	2639	1.6060	0.7348	0.6807	0.7078	0.7232	0.6883
0.5630	8.0	3016	1.6274	0.7381	0.6804	0.7092	0.7269	0.6870

Framework versions

• Transformers 5.15.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2