Delete model

model/.gitattributes

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model/1_Pooling/config.json

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-{
-  "word_embedding_dimension": 384,
-  "pooling_mode_cls_token": false,
-  "pooling_mode_mean_tokens": true,
-  "pooling_mode_max_tokens": false,
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model/README.md

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----
-language:
-- multilingual
-- ar
-- bg
-- ca
-- cs
-- da
-- de
-- el
-- en
-- es
-- et
-- fa
-- fi
-- fr
-- gl
-- gu
-- he
-- hi
-- hr
-- hu
-- hy
-- id
-- it
-- ja
-- ka
-- ko
-- ku
-- lt
-- lv
-- mk
-- mn
-- mr
-- ms
-- my
-- nb
-- nl
-- pl
-- pt
-- ro
-- ru
-- sk
-- sl
-- sq
-- sr
-- sv
-- th
-- tr
-- uk
-- ur
-- vi
-license: apache-2.0
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
-language_bcp47:
-- fr-ca
-- pt-br
-- zh-cn
-- zh-tw
-pipeline_tag: sentence-similarity
----
-
-# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-
-
-## 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 SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## 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
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-# Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, max pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-
-
-## 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/paraphrase-multilingual-MiniLM-L12-v2)
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
-
-## Citing & Authors
-
-This model was trained by [sentence-transformers](https://www.sbert.net/). 
-        
-If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
-```bibtex 
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "http://arxiv.org/abs/1908.10084",
-}
-```

model/config.json

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-{
-  "_name_or_path": "old_models/paraphrase-multilingual-MiniLM-L12-v2/0_Transformer",
-  "architectures": [
-    "BertModel"
-  ],
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-  "hidden_act": "gelu",
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-  "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "transformers_version": "4.7.0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 250037
-}

model/config_sentence_transformers.json

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-{
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-    "sentence_transformers": "2.0.0",
-    "transformers": "4.7.0",
-    "pytorch": "1.9.0+cu102"
-  }
-}

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