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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 312,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -1,3 +1,81 @@
 ---
+language:
+- ru
+
+pipeline_tag: sentence-similarity
+
+tags:
+- russian
+- pretraining
+- embeddings
+- tiny
+- feature-extraction
+- sentence-similarity
+- sentence-transformers
+- transformers
+
 license: mit
+
 ---
+
+## Быстрый Bert для Semantic text similarity (STS)
+
+Современная (на март 2024) быстрая модель BERT для расчетов компактных эмбедингов предложений на русском языке. Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), имеет аналогичный размер и быстродействие. На STS и близких задачах (PI, NLI, SA, TI) для русского языка превосходит LaBSE.
+
+Оптимальна для использования в составе RAG LLMs (при вынужденном инференсе на CPU). Для работы с контекстом свыше 512 требует дообучения под целевой домен.
+
+## Использование модели с библиотекой `transformers`:
+
+```python
+# pip install transformers sentencepiece
+import torch
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("sergeyzh/rubert-tiny-sts")
+model = AutoModel.from_pretrained("sergeyzh/rubert-tiny-sts")
+# 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,)
+```
+
+## Использование с `sentence_transformers`:
+```Python
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer('sergeyzh/rubert-tiny-sts')
+
+sentences = ["привет мир", "hello world", "здравствуй вселенная"]
+embeddings = model.encode(sentences)
+print(util.dot_score(embeddings, embeddings))
+```
+
+## Метрики
+Оценки модели на бенчмарке [encodechka](https://github.com/avidale/encodechka):
+
+| Модель                           | STS       | PI        | NLI       | SA        | TI        |
+|:---------------------------------|:---------:|:---------:|:---------:|:---------:|:---------:|
+| intfloat/multilingual-e5-large   |   0.862   |   0.727   |   0.473   |   0.810   |   0.979   |
+| Tochka-AI/ruRoPEBert-e5-base-512 |   0.793   |   0.704   |   0.457   |   0.803   |   0.970   |
+| **sergeyzh/rubert-tiny-sts**    | **0.797** | **0.702** | **0.453** | **0.778** | **0.946** |
+| cointegrated/LaBSE-en-ru         |   0.794   |   0.659   |   0.431   |   0.761   |   0.946   |
+| cointegrated/rubert-tiny2        |   0.750   |   0.651   |   0.417   |   0.737   |   0.937   |
+
+**Задачи:**
+
+- Semantic text similarity (**STS**);
+- Paraphrase identification (**PI**);
+- Natural language inference (**NLI**);
+- Sentiment analysis (**SA**);
+- Toxicity identification (**TI**).
+
+## Связанные ресурсы
+Вопросы использования модели обсуждаются в [русскоязычном чате NLP](https://t.me/natural_language_processing).
+

config.json

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+{
+  "_name_or_path": "sergeyzh/rubert-tiny-sts",
+  "architectures": [
+    "BertForPreTraining"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "emb_size": 312,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 312,
+  "initializer_range": 0.02,
+  "intermediate_size": 600,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 2048,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 83828
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69b3d1d73b5d33017d8d4dccbe84e960cff71a0c48684911624eae680839e381
+size 117513120

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 2048,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 6144,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}