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# XLM_RoBERTa-Multilingual-OpusMT-Hate-Speech-Detection

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8223
- Micro F1: 0.8057
- Macro F1: 0.8053
- Accuracy: 0.8057

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results



### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "xlm-roberta-large", "model-index": [{"name": "XLM_RoBERTa-Multilingual-OpusMT-Hate-Speech-Detection", "results": []}]}
transformers safetensors xlm-roberta text-classification generated_from_trainer base_model:xlm-roberta-large license:mit autotrain_compatible endpoints_compatible region:us