Instructions to use Denyol/FakeNews-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Denyol/FakeNews-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Denyol/FakeNews-roberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Denyol/FakeNews-roberta-large") model = AutoModelForSequenceClassification.from_pretrained("Denyol/FakeNews-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: roberta-large | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: FakeNews-roberta-large | |
| 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. --> | |
| # FakeNews-roberta-large | |
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6947 | |
| - Accuracy: 0.4766 | |
| ## 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: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.7142 | 1.0 | 1605 | 0.6954 | 0.5234 | | |
| | 0.7097 | 2.0 | 3210 | 0.6947 | 0.4766 | | |
| | 0.7033 | 3.0 | 4815 | 0.7499 | 0.4766 | | |
| | 0.691 | 4.0 | 6420 | 1.2268 | 0.4766 | | |
| | 0.6693 | 5.0 | 8025 | 1.5704 | 0.4766 | | |
| ### Framework versions | |
| - Transformers 4.34.1 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |