Text Classification
Transformers
Safetensors
English
deberta-v2
citation-verification
retrieval-augmented-generation
rag
cross-lingual
deberta
cross-encoder
nli
attribution
Eval Results (legacy)
text-embeddings-inference
Instructions to use convexray/alignment-module-cross-encoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use convexray/alignment-module-cross-encoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="convexray/alignment-module-cross-encoder-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("convexray/alignment-module-cross-encoder-base") model = AutoModelForSequenceClassification.from_pretrained("convexray/alignment-module-cross-encoder-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "microsoft/deberta-v3-large", | |
| "max_length": 384, | |
| "optimal_threshold": 0.1, | |
| "val_metrics": { | |
| "precision": 0.9803761242845462, | |
| "recall": 0.9716369529983793, | |
| "f1": 0.975986975986976, | |
| "auc": 0.996136953948406 | |
| }, | |
| "training_args": { | |
| "epochs": 5, | |
| "batch_size": 4, | |
| "learning_rate": 2e-05 | |
| } | |
| } |