# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("valurank/distilroberta-clickbait")
model = AutoModelForSequenceClassification.from_pretrained("valurank/distilroberta-clickbait")Quick Links
distilroberta-clickbait
This model is a fine-tuned version of distilroberta-base on a dataset of headlines. It achieves the following results on the evaluation set:
- Loss: 0.0268
- Acc: 0.9963
Training and evaluation data
The following data sources were used:
- 32k headlines classified as clickbait/not-clickbait from kaggle
- A dataset of headlines from https://github.com/MotiBaadror/Clickbait-Detection
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|---|---|---|---|---|
| 0.0195 | 1.0 | 981 | 0.0192 | 0.9954 |
| 0.0026 | 2.0 | 1962 | 0.0172 | 0.9963 |
| 0.0031 | 3.0 | 2943 | 0.0275 | 0.9945 |
| 0.0003 | 4.0 | 3924 | 0.0268 | 0.9963 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="valurank/distilroberta-clickbait")