Sbert Cased Finnish Paraphrase

🚀 Cased Finnish Sentence BERT model

This is a Finnish Sentence BERT model trained from FinBERT. It can be used for tasks like retrieving the most similar sentences from a large dataset.

🚀 Quick Start

This Finnish Sentence BERT is trained from FinBERT. You can find a demo on retrieving the most similar sentences from a dataset of 400 million sentences here.

✨ Features

• Language: Finnish
• Pipeline Tag: Sentence-similarity
• Tags: sentence-transformers, feature-extraction, sentence-similarity, transformers
• Widget Example: "Minusta täällä on ihana asua!"

📦 Installation

The installation is related to the libraries used in the training. You can refer to the official documentation of these libraries:

• sentence-transformers

💻 Usage Examples

Basic Usage

The usage is the same as in the HuggingFace documentation of the English Sentence Transformer. You can use it either through SentenceTransformer or HuggingFace Transformers.

SentenceTransformer

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

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)

🔧 Technical Details

Training

• Library: sentence-transformers
• FinBERT model: TurkuNLP/bert-base-finnish-cased-v1
• Data: The data provided here, 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

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})
)

📚 Documentation

Citing & Authors

While the publication is being drafted, please cite this page.

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.