Create README.md

README.md

@@ -0,0 +1,44 @@
+---
+language: ["ru"]
+tags:
+- russian
+- fill-mask
+- pretraining
+- embeddings
+- masked-lm
+- tiny
+license: mit
+widget:
+- text: "Миниатюрная модель для [MASK] разных задач."
+---
+This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. 
+
+The differences from the previous version include:
+- a larger vocabulary: 83828 tokens instead of 29564;
+- larger supported sequences: 2048 instead of 512;
+- sentence embeddings approximate LaBSE closer than before;
+- the model is focused only on Russian. 
+
+The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task.
+
+Sentence embeddings can be produced as follows:
+
+```python
+# pip install transformers sentencepiece
+import torch
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
+model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
+# model.cuda()  # uncomment it if you have a GPU
+
+def embed_bert_cls(text, model, tokenizer):
+    t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        model_output = model(**{k: v.to(model.device) for k, v in t.items()})
+    embeddings = model_output.last_hidden_state[:, 0, :]
+    embeddings = torch.nn.functional.normalize(embeddings)
+    return embeddings[0].cpu().numpy()
+
+print(embed_bert_cls('привет мир', model, tokenizer).shape)
+# (312,)
+```