metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test-trainer
results: []
language:
- en
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