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Add new SentenceTransformer model.

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+ ---
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+ language:
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+ - multilingual
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+ - ar
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+ - bg
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+ - ca
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+ - cs
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+ - da
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+ - de
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+ - el
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+ - en
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+ - es
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+ - et
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+ - fa
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+ - fi
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+ - fr
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+ - gl
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+ - gu
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+ - he
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+ - hi
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+ - hr
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+ - hu
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+ - hy
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+ - id
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+ - it
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+ - ja
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+ - ka
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+ - ko
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+ - ku
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+ - lt
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+ - lv
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+ - mk
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+ - mn
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+ - mr
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+ - ms
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+ - my
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+ - nb
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+ - nl
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+ - pl
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+ - pt
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+ - ro
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+ - ru
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+ - sk
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+ - sl
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+ - sq
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+ - sr
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+ - sv
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+ - th
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+ - tr
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+ - uk
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+ - ur
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+ - vi
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+ license: apache-2.0
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+ library_name: sentence-transformers
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+ tags:
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+ - sentence-transformers
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+ - feature-extraction
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+ - sentence-similarity
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+ - transformers
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+ language_bcp47:
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+ - fr-ca
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+ - pt-br
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+ - zh-cn
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+ - zh-tw
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+ pipeline_tag: sentence-similarity
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+ ---
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+
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+ # sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+
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+ 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.
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+
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+
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+
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+
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+ ## Usage (HuggingFace Transformers)
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+ 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.
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+ import torch
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+
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+
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+ #Mean Pooling - Take attention mask into account for correct averaging
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+ def mean_pooling(model_output, attention_mask):
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+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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+
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+
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+ # Sentences we want sentence embeddings for
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+ sentences = ['This is an example sentence', 'Each sentence is converted']
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+
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+ # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
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+ model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
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+
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+ # Tokenize sentences
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+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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+
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+ # Compute token embeddings
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+ with torch.no_grad():
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+ model_output = model(**encoded_input)
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+
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+ # Perform pooling. In this case, average pooling
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+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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+
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+ print("Sentence embeddings:")
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+ print(sentence_embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
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+
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+
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+ 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-mpnet-base-v2)
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+
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+
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+
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+ ## Full Model Architecture
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ This model was trained by [sentence-transformers](https://www.sbert.net/).
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+
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+ 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):
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+ ```bibtex
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "http://arxiv.org/abs/1908.10084",
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+ }
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+ ```
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