the first commit

README.md

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+---
+tags:
+- feature-extraction
+- embeddings
+---
+# LaBSE for English and Russian
+This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google.
+
+The current model has only English and Russian tokens left in the vocabulary.
+Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.
+ 
+To get the sentence embeddings, you can  use the following code:
+```python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
+model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
+sentences = ["Hello World", "Hallo Welt"]
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')
+with torch.no_grad():
+    model_output = model(**encoded_input)
+embeddings = model_output.pooler_output
+embeddings = torch.nn.functional.normalize(embeddings)
+print(embeddings)
+
+## Reference:
+Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020
+License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1)

config.json

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+{
+  "_name_or_path": "cointegrated/LaBSE-en-ru",
+  "architectures": [
+    "BertForPreTraining"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.5.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 55083
+}

pytorch_model.bin

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+oid sha256:d929e16c4cc9b40cdd96219e8ce3c1084129798435b3c67212efd68fa018673b
+size 516063655

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "model_max_length": 512}