Update README.md

README.md

@@ -94,6 +94,42 @@ Scores of the answers are based on the annotators decisions:
 - 2 : paragraph is partially relevant
 - 0 : paragraphs is no relevant
 
+### Evaluation of an embedding model
+
+For evaluation of an embedding model with this dataset, you can use HF datasets and  BEIR toolit:
+
+Example of evaluation of a model:
+
+```python
+from beir import util, LoggingHandler
+from beir.retrieval import models
+from beir.datasets.data_loader import GenericDataLoader
+from beir.retrieval.evaluation import EvaluateRetrieval
+from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES
+from huggingface_hub import snapshot_download
+import logging
+import pathlib, os
+
+#### Just some code to print debug information to stdout
+logging.basicConfig(format='%(asctime)s - %(message)s',
+                    datefmt='%Y-%m-%d %H:%M:%S',
+                    level=logging.INFO,
+                    handlers=[LoggingHandler()])
+#
+data_path  = snapshot_download(repo_id="TUKE-KEMT/retrieval-skquad",repo_type="dataset")
+model_path = "TUKE-DeutscheTelekom/slovakbert-skquad-mnlr"
+
+model = DRES(models.SentenceBERT(model_path), batch_size=16)
+
+corpus, queries, qrels = GenericDataLoader(data_folder=data_path).load(split="test")
+
+#### Load the SBERT model and retrieve using cosine-similarity
+retriever = EvaluateRetrieval(model, score_function="dot") # or "cos_sim" for cosine similarity
+results = retriever.retrieve(corpus, queries)
+
+#### Evaluate your model with NDCG@k, MAP@K, Recall@K and Precision@K  where k = [1,3,5,10,100,1000]
+ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)
+```
 
 ### Database Content