christinacdl/XLM_RoBERTa-Clickbait-Detection-new

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XLM_RoBERTa-Clickbait-Detection-new

This model is a fine-tuned version of xlm-roberta-large on the christinacdl/clickbaitdetectiondataset 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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 32
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 4
• —early stopping patience: 2
• —adam epsilon: 1e-8
• —gradient_checkpointing: True
• —maxgradnorm: 1.0
• —seed: 42
• —optimizer: adamwtorchfused
• —weight decay: 0.01
• —warmup_ratio: 0
• —groupbylength: True
• —maxseqlength: 512
• —save_steps: 1000
• —logging_steps: 500
• —evaluation_strategy: epoch
• —save_strategy: epoch
• —eval_steps: 1000
• —savetotallimit: 2

All results from Training and Evaluation

• —"epoch": 4.0,
• —"eval_accuracy": 0.9844203855294428,
• —"eval_loss": 0.08027808368206024,
• —"evalmacrof1": 0.9843695357857132,
• —"evalmicrof1": 0.9844203855294428,
• —"eval_runtime": 124.9733,
• —"eval_samples": 3787,
• —"evalsamplesper_second": 30.302,
• —"evalstepsper_second": 1.896,
• —"predict_accuracy": 0.9838922630050172,
• —"predict_loss": 0.07716809958219528,
• —"predictmacrof1": 0.9838416247418498,
• —"predictmicrof1": 0.9838922630050172,
• —"predict_runtime": 127.7861,
• —"predict_samples": 3787,
• —"predictsamplesper_second": 29.635,
• —"predictstepsper_second": 1.855,
• —"train_loss": 0.057462599486458765,
• —"train_runtime": 25253.576,
• —"train_samples": 30296,
• —"trainsamplesper_second": 4.799,
• —"trainstepsper_second": 0.15

Framework versions

• —Transformers 4.36.1
• —Pytorch 2.1.0+cu121
• —Datasets 2.13.1
• —Tokenizers 0.15.0