readme: ---
  language:
  - fi
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  widget:
  - text: "Minusta täällä on ihana asua!"
  ---
  
  
  # Cased Finnish Sentence BERT model
  
  Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found [here](http://epsilon-it.utu.fi/sbert400m).
  
  ## Training
  
  - Library: [sentence-transformers](https://www.sbert.net/)
  - FinBERT model: TurkuNLP/bert-base-finnish-cased-v1
  - Data: The data provided [here](https://turkunlp.org/paraphrase.html), including the Finnish Paraphrase Corpus and the automatically collected paraphrase candidates (500K positive and 5M negative)
  - Pooling: mean pooling
  - Task: Binary prediction, whether two sentences are paraphrases or not. Note: the labels 3 and 4 are considered paraphrases, and labels 1 and 2 non-paraphrases. [Details on labels](https://aclanthology.org/2021.nodalida-main.29/)
  
  ## Usage
  
  The same as in the HuggingFace documentation of [the English Sentence Transformer](https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens). Either through `SentenceTransformer` or `HuggingFace Transformers`
  
  ### SentenceTransformer
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
  
  model = SentenceTransformer('TurkuNLP/sbert-cased-finnish-paraphrase')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  ### HuggingFace Transformers
  
  ```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 = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
  model = AutoModel.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
  
  # 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, mean pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  ## Evaluation Results
  
  A publication detailing the evaluation results is currently being drafted.
  
  ## Full Model Architecture
  
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
    (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})
  )
  ```
  
  ## Citing & Authors
  
  While the publication is being drafted, please cite [this page](https://turkunlp.org/paraphrase.html).
  
  ## References
  
  - J. Kanerva, F. Ginter, LH. Chang, I. Rastas, V. Skantsi, J. Kilpeläinen, HM. Kupari, J. Saarni, M. Sevón, and O. Tarkka. Finnish Paraphrase Corpus. In *NoDaLiDa 2021*, 2021.
  - N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In *EMNLP-IJCNLP*, pages 3982–3992, 2019.
  - A. Virtanen, J. Kanerva, R. Ilo, J. Luoma, J. Luotolahti, T. Salakoski, F. Ginter, and S. Pyysalo. Multilingual is not enough: BERT for Finnish. *arXiv preprint arXiv:1912.07076*, 2019.