98 lines
3.8 KiB
Markdown
98 lines
3.8 KiB
Markdown
---
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language:
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- fi
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pipeline_tag: sentence-similarity
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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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widget:
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- text: "Minusta täällä on ihana asua!"
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---
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# Cased Finnish Sentence BERT model
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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).
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## Training
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- Library: [sentence-transformers](https://www.sbert.net/)
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- FinBERT model: TurkuNLP/bert-base-finnish-cased-v1
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- 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)
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- Pooling: mean pooling
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- 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/)
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## Usage
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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`
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### SentenceTransformer
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
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model = SentenceTransformer('TurkuNLP/sbert-cased-finnish-paraphrase')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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### HuggingFace Transformers
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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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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# Sentences we want sentence embeddings for
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sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
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model = AutoModel.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
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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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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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A publication detailing the evaluation results is currently being drafted.
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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: BertModel
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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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## Citing & Authors
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While the publication is being drafted, please cite [this page](https://turkunlp.org/paraphrase.html).
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## References
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- 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.
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- N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In *EMNLP-IJCNLP*, pages 3982–3992, 2019.
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- 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. |