Text Classification
Transformers
ONNX
English
deberta-v2
code
decision-memory
sequence-classification
encoder
deberta-v3
int8
quantized
on-device
memtrace
cortex
aletheia
Eval Results (legacy)
text-embeddings-inference
Instructions to use memtrace/aletheia-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memtrace/aletheia-1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memtrace/aletheia-1.5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memtrace/aletheia-1.5") model = AutoModelForSequenceClassification.from_pretrained("memtrace/aletheia-1.5") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model": "aletheia-1.5", | |
| "base": "microsoft/deberta-v3-large", | |
| "quant": "int8-dynamic", | |
| "temperature": 0.7837, | |
| "threshold_default": 0.61, | |
| "resident_mb": 1200, | |
| "test_auc_commit": 0.832, | |
| "test_auc_conv": 0.933, | |
| "val_precision": 0.88, | |
| "val_recall": 0.798 | |
| } |