Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use caush/Clickbait2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caush/Clickbait2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="caush/Clickbait2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("caush/Clickbait2") model = AutoModelForSequenceClassification.from_pretrained("caush/Clickbait2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Clickbait2 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Clickbait2 | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0212 | |
| ## 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: 5e-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: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 0.05 | 50 | 0.0213 | | |
| | No log | 0.09 | 100 | 0.0213 | | |
| | No log | 0.14 | 150 | 0.0213 | | |
| | No log | 0.18 | 200 | 0.0216 | | |
| | No log | 0.23 | 250 | 0.0214 | | |
| | No log | 0.27 | 300 | 0.0212 | | |
| | No log | 0.32 | 350 | 0.0214 | | |
| | No log | 0.36 | 400 | 0.0212 | | |
| | No log | 0.41 | 450 | 0.0218 | | |
| | 0.0219 | 0.46 | 500 | 0.0219 | | |
| | 0.0219 | 0.5 | 550 | 0.0214 | | |
| | 0.0219 | 0.55 | 600 | 0.0216 | | |
| | 0.0219 | 0.59 | 650 | 0.0217 | | |
| | 0.0219 | 0.64 | 700 | 0.0214 | | |
| | 0.0219 | 0.68 | 750 | 0.0214 | | |
| | 0.0219 | 0.73 | 800 | 0.0214 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.11.0a0+17540c5 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.12.1 | |