Text Ranking
Transformers
Safetensors
sentence-transformers
English
modernbert
text-classification
cross-encoder
text-embeddings-inference
Instructions to use Jsevisal/CrossEncoder-ModernBERT-base-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/CrossEncoder-ModernBERT-base-qnli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/CrossEncoder-ModernBERT-base-qnli") model = AutoModelForSequenceClassification.from_pretrained("Jsevisal/CrossEncoder-ModernBERT-base-qnli") - sentence-transformers
How to use Jsevisal/CrossEncoder-ModernBERT-base-qnli with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Jsevisal/CrossEncoder-ModernBERT-base-qnli") 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) - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and tags to `sentence-transformers`
#1
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Update metadata to set pipeline tag to the new
text-ranking - Update metadata to set tags to
sentence-transformers
Changes
This is an automated pull request to update the metadata of the model card. We recently introduced the text-ranking pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them. I also updated added metadata to specify that this model can be loaded with the sentence-transformers library, as it should be possible to load any model compatible with transformers AutoModelForSequenceClassification.
Feel free to verify that it works with the following:
pip install sentence-transformers
from sentence_transformers import CrossEncoder
model = CrossEncoder("Jsevisal/CrossEncoder-ModernBERT-base-qnli")
scores = model.predict([
("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
Feel free to respond if you have questions or concerns.
- Tom Aarsen
Jsevisal changed pull request status to merged