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# XLM_RoBERTa-Clickbait-Detection-new

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the christinacdl/clickbait_detection_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1071
- Micro F1: 0.9834
- Macro F1: 0.9833
- Accuracy: 0.9834

It achieves the following results on the test set:
- Accuracy: 0.9838922630050172
- Micro-F1 Score: 0.9838922630050172
- Macro-F1 Score: 0.9838416247418498
- Matthews Correlation Coefficient: 0.9676867009951606

- Precision of each class: [0.98156425 0.98597897]
- Recall of each class: [0.98431373 0.98351648]
- F1 score of each class: [0.98293706 0.98474619]

## Intended uses & limitations

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
- early stopping patience: 2
- adam epsilon: 1e-8
- gradient_checkpointing: True
- max_grad_norm: 1.0
- seed: 42
- optimizer: adamw_torch_fused
- weight decay: 0.01
- warmup_ratio: 0
- group_by_length: True
- max_seq_length: 512
- save_steps: 1000                
- logging_steps: 500
- evaluation_strategy: epoch
- save_strategy: epoch
- eval_steps: 1000
- save_total_limit: 2


### All results from Training and Evaluation
- "epoch": 4.0,
- "eval_accuracy": 0.9844203855294428,
- "eval_loss": 0.08027808368206024,
- "eval_macro_f1": 0.9843695357857132,
- "eval_micro_f1": 0.9844203855294428,
- "eval_runtime": 124.9733,
- "eval_samples": 3787,
- "eval_samples_per_second": 30.302,
- "eval_steps_per_second": 1.896,
- "predict_accuracy": 0.9838922630050172,
- "predict_loss": 0.07716809958219528,
- "predict_macro_f1": 0.9838416247418498,
- "predict_micro_f1": 0.9838922630050172,
- "predict_runtime": 127.7861,
- "predict_samples": 3787,
- "predict_samples_per_second": 29.635,
- "predict_steps_per_second": 1.855,
- "train_loss": 0.057462599486458765,
- "train_runtime": 25253.576,
- "train_samples": 30296,
- "train_samples_per_second": 4.799,
- "train_steps_per_second": 0.15


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
{"language": ["en", "el", "ru", "ro", "de", "it", "es"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["christinacdl/clickbait_detection_dataset"], "metrics": ["accuracy", "f1", "recall", "precision"], "base_model": "xlm-roberta-large", "pipeline_tag": "text-classification", "model-index": [{"name": "XLM_RoBERTa-Clickbait-Detection-new", "results": []}]}
transformers safetensors xlm-roberta text-classification generated_from_trainer en el ru ro de it es dataset:christinacdl/clickbait_detection_dataset base_model:xlm-roberta-large license:mit autotrain_compatible endpoints_compatible has_space region:us