Text Classification
sentence-transformers
Safetensors
English
deberta-v2
cross-encoder
reranker
Generated from Trainer
dataset_size:102836
loss:CrossEntropyLoss
text-embeddings-inference
Instructions to use software-si/kitchen-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use software-si/kitchen-nli with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("software-si/kitchen-nli") 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
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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## 🧾 Input / Output
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This a model for Natural Language Inference NLI. it take a premises and an hypothesis as input, and return a classification of the relationship between the two input sentence
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Possible outputs are: contradiction, entailment, neutral
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**Example:**
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- premises:
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`kitchen eighty centimeters wide, deep 70 cm placed on closed compartment`
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- hypothesis:
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`the kitchen is placed on open shelf`
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- Output:
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`contradiction`
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---
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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