Update README.md

README.md

@@ -1,3 +1,72 @@
 ---
+pipeline_tag: text-classification
+tags:
+- transformers
+- information-retrieval
+language: pl
 license: apache-2.0
+
 ---
+
+<h1 align="center">polish-reranker-large-mse</h1>
+
+This is a Polish text ranking model trained using the mean squared error (MSE) distillation method on a large dataset of text pairs consisting of 1.4 million queries and 10 million documents. 
+The training data included the following parts: 1) The Polish MS MARCO training split (800k queries); 2) The ELI5 dataset translated to Polish (over 500k queries); 3) A collection of Polish medical questions and answers (approximately 100k queries).
+As a teacher model, we employed [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k), a large multilingual reranker based on the MT5-XXL architecture. As a student model, we choose [Polish RoBERTa](https://huggingface.co/sdadas/polish-roberta-large-v2).
+In the MSE method, the student is trained to directly replicate the outputs returned by the teacher.
+
+## Usage (Sentence-Transformers)
+
+You can use the model like this with [sentence-transformers](https://www.SBERT.net):
+
+```python
+from sentence_transformers import CrossEncoder
+import torch.nn
+
+query = "Jak dożyć 100 lat?"
+answers = [
+    "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."
+]
+
+model = CrossEncoder(
+    "sdadas/polish-reranker-large-mse",
+    default_activation_function=torch.nn.Identity(),
+    max_length=512,
+    device="cuda" if torch.cuda.is_available() else "cpu"
+)
+pairs = [[query, answer] for answer in answers]
+results = model.predict(pairs)
+print(results.tolist())
+```
+
+## Usage (Huggingface Transformers)
+
+The model can also be used with Huggingface Transformers in the following way:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+query = "Jak dożyć 100 lat?"
+answers = [
+    "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."
+]
+
+model_name = "sdadas/polish-reranker-large-mse"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+texts = [f"{query}</s></s>{answer}" for answer in answers]
+tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt")
+output = model(**tokens)
+results = output.logits.detach().numpy()
+results = np.squeeze(results)
+print(results.tolist())
+```
+
+## Evaluation Results
+
+The model achieves **NDCG@10** of **60.27** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results.