Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+language: pl
 license: apache-2.0
+widget:
+- source_sentence: "zapytanie: Jak dożyć 100 lat?"
+  sentences:
+    - "Trzeba zdrowo się odżywiać i uprawiać sport."
+    - "Trzeba pić alkohol, imprezować i jeździć szybkimi autami."
+    - "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
+
 ---
+
+<h1 align="center">MMLW-roberta-base</h1>
+
+MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish.
+This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning. 
+It transforms texts to 768 dimensional vectors. 
+The model was initialized with Polish RoBERTa checkpoint, and then trained with [multilingual knowledge distillation method](https://aclanthology.org/2020.emnlp-main.365/) on a diverse corpus of 60 million Polish-English text pairs. We utilised [English FlagEmbeddings (BGE)](https://huggingface.co/BAAI/bge-base-en) as teacher models for distillation. 
+
+## Usage (Sentence-Transformers)
+
+⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix **"zapytanie: "** ⚠️
+
+You can use the model like this with [sentence-transformers](https://www.SBERT.net):
+
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+query_prefix = "zapytanie: "
+answer_prefix = ""
+queries = [query_prefix + "Jak dożyć 100 lat?"]
+answers = [
+    answer_prefix + "Trzeba zdrowo się odżywiać i uprawiać sport.",
+    answer_prefix + "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.",
+    answer_prefix + "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
+]
+model = SentenceTransformer("sdadas/mmlw-roberta-base")
+queries_emb = model.encode(queries, convert_to_tensor=True, show_progress_bar=False)
+answers_emb = model.encode(answers, convert_to_tensor=True, show_progress_bar=False)
+
+best_answer = cos_sim(queries_emb, answers_emb).argmax().item()
+print(answers[best_answer])
+# Trzeba zdrowo się odżywiać i uprawiać sport.
+```
+
+## Evaluation Results
+
+The model achieves **NDCG@10** of **53.60** on the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results.
+
+## Acknowledgements
+This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.