Text Classification
Transformers
ONNX
English
qwen2
code
decision-memory
sequence-classification
int8
quantized
memtrace
cortex
aletheia
Eval Results (legacy)
text-embeddings-inference
Instructions to use memtrace/aletheia-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memtrace/aletheia-1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memtrace/aletheia-1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memtrace/aletheia-1.0") model = AutoModelForSequenceClassification.from_pretrained("memtrace/aletheia-1.0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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