Text Classification
Transformers
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use AeglosAI/aeglos-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AeglosAI/aeglos-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AeglosAI/aeglos-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AeglosAI/aeglos-v1") model = AutoModelForSequenceClassification.from_pretrained("AeglosAI/aeglos-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - text-classification | |
| widget: | |
| - text: "I love AutoTrain" | |
| datasets: | |
| - beloiual/autotrain-data-llm-prompt-injection-classifier-v2 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 0.22951629757881165 | |
| f1: 0.9444444444444444 | |
| precision: 0.9577464788732394 | |
| recall: 0.9315068493150684 | |
| auc: 0.9905251141552511 | |
| accuracy: 0.9585492227979274 | |