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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,197 +1,80 @@
 ---
-language:
-- multilingual
-- af
-- sq
-- am
-- ar
-- hy
-- as
-- az
-- eu
-- be
-- bn
-- bs
-- bg
-- my
-- ca
-- ceb
-- zh
-- co
-- hr
-- cs
-- da
-- nl
-- en
-- eo
-- et
-- fi
-- fr
-- fy
-- gl
-- ka
-- de
-- el
-- gu
-- ht
-- ha
-- haw
-- he
-- hi
-- hmn
-- hu
-- is
-- ig
-- id
-- ga
-- it
-- ja
-- jv
-- kn
-- kk
-- km
-- rw
-- ko
-- ku
-- ky
-- lo
-- la
-- lv
-- lt
-- lb
-- mk
-- mg
-- ms
-- ml
-- mt
-- mi
-- mr
-- mn
-- ne
-- no
-- ny
-- or
-- fa
-- pl
-- pt
-- pa
-- ro
-- ru
-- sm
-- gd
-- sr
-- st
-- sn
-- si
-- sk
-- sl
-- so
-- es
-- su
-- sw
-- sv
-- tl
-- tg
-- ta
-- tt
-- te
-- th
-- bo
-- tr
-- tk
-- ug
-- uk
-- ur
-- uz
-- vi
-- cy
-- wo
-- xh
-- yi
-- yo
-- zu
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-- transformers
-license: apache-2.0
+
 ---
-# LaBSE_geonames_v1 (ru)
 
-## Описание
-Это порт модели [LaBSE](https://tfhub.dev/google/LaBSE/1) для PyTorch, который позволяет отображать текст на 109 языках в общее векторное пространство.
-Эта модель используется для поиска похожих географических названий на основе введенного запроса.
-С помощью функции можно кодировать запрос в векторное пространство, и искать наиболее близкие эмбеддинги и возвращает результаты в удобном формате.
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
 
-## Установка
-Для использования этой модели рекомендуется установить пакет `sentence-transformers`:
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
 pip install -U sentence-transformers
 ```
 
-## Использование
-Пример использования модели:
+Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('wasjaip/LaBSE_geonames_v1')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-## Оценка Модели
-Подробности автоматической оценки модели можно найти на Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/LaBSE)
 
-## Архитектура Модели
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
-  (3): Normalize()
-)
-```
-
-## Более подробная информация и публикация, описывающая LaBSE, доступна на [LaBSE](https://tfhub.dev/google/LaBSE/1) 
 
+## Evaluation Results
 
+<!--- Describe how your model was evaluated -->
 
-# LaBSE_geonames_v1 (en)
-This is a port of the [LaBSE](https://tfhub.dev/google/LaBSE/1) model to PyTorch. It can be used to map 109 languages to a shared vector space.
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
 
-## Usage (Sentence-Transformers)
+## Training
+The model was trained with the parameters:
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+**DataLoader**:
 
+`torch.utils.data.dataloader.DataLoader` of length 9661 with parameters:
 ```
-pip install -U sentence-transformers
+{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
-Then you can use the model like this:
+**Loss**:
 
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
 
-model = SentenceTransformer('wasjaip/LaBSE_geonames_v1')
-embeddings = model.encode(sentences)
-print(embeddings)
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 6,
+    "evaluation_steps": 5000,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 100,
+    "weight_decay": 0.01
+}
 ```
-
-
-
-## Evaluation Results
-
-
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/LaBSE)
-
 
 
 ## Full Model Architecture
@@ -206,6 +89,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-Have a look at [LaBSE](https://tfhub.dev/google/LaBSE/1) for the respective publication that describes LaBSE.
-
-
+<!--- Describe where people can find more information -->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_LaBSE/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

model.safetensors

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+size 1883730160

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer.json

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+size 13632172

tokenizer_config.json

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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  },
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+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "full_tokenizer_file": null,
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+  "pad_token": "[PAD]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}