cryptocurrency-intent-search-detection

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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

Framework versions

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