STL-xlm-roberta-large-sentiment

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

• Loss: 1.2339
• Sentiment Precision: 0.7323
• Sentiment Recall: 0.7313
• F1: 0.7317
• Sentiment Acc: 0.7369

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	Sentiment Precision	Sentiment Recall	F1	Sentiment Acc
0.8659	1.0	377	0.7622	0.6997	0.6928	0.6943	0.6983
0.7560	2.0	754	0.6376	0.7381	0.7352	0.7284	0.7369
0.5973	3.0	1131	0.6430	0.7265	0.7269	0.7203	0.7319
0.4718	4.0	1508	0.6938	0.7301	0.7304	0.7281	0.7394
0.3951	5.0	1885	0.6745	0.7335	0.7357	0.7323	0.7394
0.2933	6.0	2262	0.8124	0.7382	0.7358	0.7366	0.7406
0.2386	7.0	2639	0.9305	0.7232	0.7229	0.7228	0.7294
0.1537	8.0	3016	1.1450	0.7301	0.7288	0.7290	0.7357
0.1615	9.0	3393	1.2339	0.7323	0.7313	0.7317	0.7369

Framework versions

• Transformers 5.16.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.23.1