---
language:
- en
license: mit
model-index:
- name: iamgroot42/spice
  results:
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      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
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    - type: main_score
      value: 72.962
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    metrics:
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      value: 63.056999999999995
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    - type: recall_at_20
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    - type: recall_at_1000
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    task:
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  - dataset:
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      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
      split: test
      type: mteb/cqadupstack-english
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    task:
      type: Retrieval
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
---

## SPICE

Selective Pretraining for Informed Context Extraction (SPICE)- a small but powerful retriever. 

>  The future is a thing that is not given. It is a thing that is made." - Leto II (Dune)

<img src="./leto2.webp" alt="SPICE retriever logo. Generated using DALL-E" width="200"/>

### Using the model

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("iamgroot42/spice")
sentences = [
  "Beware of manuals. Manuals create habits.",
  "How easy it is to mistake clear reasoning from correct reasoning!",
  "Show me a completely smooth operation and I'll show you someone who's covering mistakes. Real boats rock."
]

embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
```


Model training details and data will be uploaded soon!