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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# test-trainer

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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