Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use AutoCyberAI/crp-prm-deberta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutoCyberAI/crp-prm-deberta-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AutoCyberAI/crp-prm-deberta-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AutoCyberAI/crp-prm-deberta-v1") model = AutoModelForSequenceClassification.from_pretrained("AutoCyberAI/crp-prm-deberta-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
crp-prm-deberta-v1
This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6928
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4069 | 1.0 | 500 | 0.6928 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.13.0+cpu
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for AutoCyberAI/crp-prm-deberta-v1
Base model
microsoft/deberta-v3-small