Text Ranking
sentence-transformers
Safetensors
Transformers
Arabic
xlm-roberta
text-classification
text-embeddings-inference
Instructions to use Omartificial-Intelligence-Space/ARA-Reranker-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Omartificial-Intelligence-Space/ARA-Reranker-V1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Omartificial-Intelligence-Space/ARA-Reranker-V1") 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 Omartificial-Intelligence-Space/ARA-Reranker-V1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/ARA-Reranker-V1") model = AutoModelForSequenceClassification.from_pretrained("Omartificial-Intelligence-Space/ARA-Reranker-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
Verified signatureHello! ## Pull Request overview * Update metadata to set pipeline tag to the new `text-ranking` * Update metadata to set library name to `sentence-transformers` ## Changes This is an automated pull request to update the metadata of the model card. We recently introduced the [`text-ranking`](https://huggingface.co/models?pipeline_tag=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: ```bash pip install sentence-transformers ``` ```python from sentence_transformers import CrossEncoder model = CrossEncoder("Omartificial-Intelligence-Space/ARA-Reranker-V1") scores = model.predict([ ("How many people live in Berlin?", "Berlin had a…5eed646
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