Create README.md

README.md

@@ -1,3 +1,24 @@
----
-license: cc-by-4.0
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+You can use transformer library and load model for conditional generation and expect those tokens or use monoT5 implementation from BEIR.
+
+prompt = `Query: {query} Document: {document} Relevant:`
+
+Model returns tokens if relevant or not:
+``` token_false='▁fałsz', token_true='▁prawda'```
+
+
+MonoT5 implementation is included in BEIR benchmark(https://github.com/beir-cellar/beir):
+```
+from beir.reranking.models import MonoT5
+from beir.reranking import Rerank
+
+queries = YOUR_QUERIES
+corpus = YOUR_CORPUS
+queries = {query['id'] : query['text'] for query in queries}
+corpus = {doc['id']: {'title': doc['title'] , 'text': doc['text']} for doc in corpus}
+
+
+cross_encoder_model = MonoT5(model_path, use_amp=False, token_false='▁fałsz', token_true='▁prawda')
+reranker = Rerank(cross_encoder_model, batch_size=100)
+
+rerank_results = reranker.rerank(corpus, queries, results, top_k=100)
+```