---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: training_dir
  results: []
---

<!-- 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. -->

# training_dir

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4296
- Accuracy: 0.8650
- F1: 0.8647
- Precision: 0.8662
- Recall: 0.8650
- Accuracy Label Communication Issue: 0.3878
- Accuracy Label General Query: 0.5135
- Accuracy Label Other: 0.8535
- Accuracy Label Praise: 0.7987
- Accuracy Label Service Issue: 0.9503

## 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: 5e-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 | F1     | Precision | Recall | Accuracy Label Communication Issue | Accuracy Label General Query | Accuracy Label Other | Accuracy Label Praise | Accuracy Label Service Issue |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------------------:|:----------------------------:|:--------------------:|:---------------------:|:----------------------------:|
| 0.4926        | 1.0   | 220  | 0.4769          | 0.8351   | 0.7977 | 0.7654    | 0.8351 | 0.0                                | 0.0                          | 0.8479               | 0.7315                | 0.9780                       |
| 0.4722        | 2.0   | 440  | 0.4490          | 0.8138   | 0.8335 | 0.8685    | 0.8138 | 0.5918                             | 0.2973                       | 0.8592               | 0.8523                | 0.8358                       |
| 0.1949        | 3.0   | 660  | 0.4296          | 0.8650   | 0.8647 | 0.8662    | 0.8650 | 0.3878                             | 0.5135                       | 0.8535               | 0.7987                | 0.9503                       |


### Framework versions

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