---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- f1
model-index:
- name: test_emotion_detection_gersti
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.47309833024118736
    - name: F1
      type: f1
      value: 0.14226543980966658
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test_emotion_detection_gersti

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1084
- Accuracy: 0.4731
- F1: 0.1423

## 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: 1e-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: 10

### Training results



### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3