Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/roberta-base-disaster-tweet-jailbreaking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/roberta-base-disaster-tweet-jailbreaking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/roberta-base-disaster-tweet-jailbreaking")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/roberta-base-disaster-tweet-jailbreaking") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/roberta-base-disaster-tweet-jailbreaking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-base-disaster-tweet-jailbreaking
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6054
- Accuracy: 0.7155
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5181 | 1.0 | 243 | 0.6217 | 0.6948 |
| 0.5035 | 2.0 | 486 | 0.6114 | 0.6845 |
| 0.5149 | 3.0 | 729 | 0.6036 | 0.7175 |
| 0.3626 | 4.0 | 972 | 0.7024 | 0.7072 |
| 0.2085 | 5.0 | 1215 | 0.8520 | 0.7196 |
| 0.3198 | 6.0 | 1458 | 1.2850 | 0.7134 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
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Model tree for leomaurodesenv/roberta-base-disaster-tweet-jailbreaking
Base model
FacebookAI/roberta-base