DiTy
/

cross-encoder-russian-msmarco

@@ -1,201 +1,87 @@
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 ---
+library_name: sentence-transformers
+pipeline_tag: text-classification
+tags:
+- sentence-transformers
+- text-classification
+- transformers
+- rubert
+- cross-encoder
+- msmarco
+datasets:
+- unicamp-dl/mmarco
+language:
+- ru
+base_model: DeepPavlov/rubert-base-cased
+widget:
+- text: "как часто нужно ходить к стоматологу? [SEP] Дядя Женя работает врачем стоматологом."
+  example_title: "Example 1"
+- text: "как часто нужно ходить к стоматологу? [SEP] Минимальный обязательный срок посещения зубного врача – раз в год, но специалисты рекомендуют делать это чаще – раз в полгода, а ещё лучше – раз в квартал. При таком сроке легко отследить любые начинающиеся проблемы и исправить их сразу же."
+  example_title: "Example 2"
 ---
+# DiTy/cross-encoder-russian-msmarco
+This is a [sentence-transformers](https://www.SBERT.net) model based on a pre-trained [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) and finetuned with [MS-MARCO Russian passage ranking dataset](https://huggingface.co/datasets/unicamp-dl/mmarco).
+The model can be used for Information Retrieval in the Russian language: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details.
+<!--- Describe your model here -->
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+```
+pip install -U sentence-transformers
+```
+Then you can use the model like this:
+```python
+from sentence_transformers import CrossEncoder
+reranker_model = CrossEncoder('DiTy/cross-encoder-russian-msmarco', device='cuda')
+query = ["как часто нужно ходить к стоматологу?"]
+documents = [
+    "Минимальный обязательный срок посещения зубного врача – раз в год, но специалисты рекомендуют делать это чаще – раз в полгода, а ещё лучше – раз в квартал. При таком сроке легко отследить любые начинающиеся проблемы и исправить их сразу же.",
+    "Основная причина заключается в истончении поверхностного слоя зуба — эмали, которая защищает зуб от механических, химических и температурных воздействий. Под эмалью расположен дентин, который более мягкий по своей структуре и пронизан множеством канальцев. При повреждении эмали происходит оголение дентинных канальцев. Раздражение с них начинает передаваться на нервные окончания в зубе и возникают болевые ощущения. Чаще всего дентин оголяется в придесневой области зубов, поскольку эмаль там наиболее тонкая и стирается быстрее.",
+    "Стоматолог, также известный как стоматолог-хирург, является медицинским работником, который специализируется на стоматологии, отрасли медицины, специализирующейся на зубах, деснах и полости рта.",
+    "Дядя Женя работает врачем стоматологом",
+    "Плоды малины употребляют как свежими, так и замороженными или используют для приготовления варенья, желе, мармелада, соков, а также ягодного пюре. Малиновые вина, наливки, настойки, ликёры обладают высокими вкусовыми качествами.",
+]
+predict_result = reranker_model.predict([[query[0], documents[0]]])
+print(predict_result)
+# `array([0.88126713], dtype=float32)`
+rank_result = reranker_model.rank(query[0], documents)
+print(rank_result)
+# `[{'corpus_id': 0, 'score': 0.88126713},
+#  {'corpus_id': 2, 'score': 0.001042091},
+#  {'corpus_id': 3, 'score': 0.0010417715},
+#  {'corpus_id': 1, 'score': 0.0010344835},
+#  {'corpus_id': 4, 'score': 0.0010244923}]`
+```
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+```python
+# import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained('DiTy/cross-encoder-russian-msmarco')
+tokenizer = AutoTokenizer.from_pretrained('DiTy/cross-encoder-russian-msmarco')
+features = tokenizer(["как часто нужно ходить к стоматологу?", "как часто нужно ходить к стоматологу?"], ["Минимальный обязательный срок посещения зубного врача – раз в год, но специалисты рекомендуют делать это чаще – раз в полгода, а ещё лучше – раз в квартал. При таком сроке легко отследить любые начинающиеся проблемы и исправить их сразу же.", "Дядя Женя работает врачем стоматологом"], padding=True, truncation=True, return_tensors='pt')
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    print(scores)
+# `tensor([[ 1.6871],
+        [-6.8700]])`
+```