Create README.md

README.md

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+---
+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-sfle256 ', max_length=256)
+
+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-sfle256 ', max_length=256)
+
+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']}")
+```