augmented_model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3758
• Accuracy: 0.5430
• F1: 0.5434
• Precision: 0.5446
• Recall: 0.5426

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: 3e-06
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.3112	0.3131	500	0.8670	0.7334	0.7229	0.7271	0.7246
0.3427	0.6262	1000	0.8683	0.7338	0.7232	0.7271	0.7250
0.3817	0.9393	1500	0.8327	0.7356	0.7250	0.7295	0.7268
0.3212	1.2523	2000	0.8733	0.7312	0.7206	0.7247	0.7224
0.2992	1.5654	2500	0.8882	0.7308	0.7206	0.7245	0.7222
0.2963	1.8785	3000	0.8918	0.7308	0.7209	0.7244	0.7224

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1