dadashzadeh/cryptocurrency-intent-search-detection

test-trainer

This model is a fine-tuned version of xlm-roberta-base on the cryptocurrency dataset. It achieves the following results on the evaluation set:

• Loss: 0.2337
• Accuracy: 0.9169

Model description

intent search detection :

Navigational: Users want to find a specific page (e.g., “reddit login”) Informational: Users want to learn more about something (e.g., “what is seo”) Commercial: Users want to do research before making a purchase decision (e.g., “best coffee maker”) Transactional: Users want to complete a specific action, usually a purchase (e.g., “buy subaru forester”)

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3629	1.0	14391	0.3249	0.8866
0.313	2.0	28782	0.2640	0.9067
0.2723	3.0	43173	0.2337	0.9169

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1