---
license: cc-by-nc-2.0
language:
- cs
base_model:
- fav-kky/FERNET-C5
---

This is fav-kky/FERNET-C5, fine-tuned with the **Cross-Encoder** architecture on the Czech News Dataset for Semantic Textual Similarity and DaReCzech. The Cross-Encoder architecture processes both input text pieces simultaneously, enabling better accuracy.

The model can be used both for Semantic Textual Similarity and re-ranking.

**Semantic Textual Similarity**: The model takes two input sentences and evaluates how similar their meanings are.

```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('ctu-aic/CE-fernet-c5-sfle512', max_length=512)

scores = model.predict([["sentence_1", "sentence_2"]])
print(scores)

```

**Re-ranking task**: Given a query, the model assesses all potential passages and ranks them in descending order of relevance.


```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('ctu-aic/CE-fernet-c5-sfle512', max_length=512)

query = "Example query for."

documents = [
    "Example document one.",
    "Example document two.",
    "Example document three."
]

top_k = 3
return_documents = True

results = model.rank(
    query=query,
    documents=documents,
    top_k=top_k,
    return_documents=return_documents
)

for i, res in enumerate(results):
    print(f"{i+1}. {res['text']}")
```