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
File size: 341 Bytes
32f322b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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
}
} |