Text Ranking
sentence-transformers
ONNX
Safetensors
OpenVINO
Transformers
English
electra
text-classification
custom_code
Instructions to use cross-encoder/monoelectra-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/monoelectra-large with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder/monoelectra-large", trust_remote_code=True) query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use cross-encoder/monoelectra-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/monoelectra-large", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/monoelectra-large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
9570843 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:958f68861ab6e78910756d76e866015f410f643fa00ae38dcb02d9decc5d2e5c
size 338235452
|