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Model: TurkuNLP/sbert-uncased-finnish-paraphrase
Source: Original Platform

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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
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+}

README.md

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+---
+language:
+- fi
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+widget:
+- text: "Minusta täällä on ihana asua!"
+---
+
+
+# Uncased 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 *using [the cased model](https://huggingface.co/TurkuNLP/sbert-cased-finnish-paraphrase)* can be found [here](http://epsilon-it.utu.fi/sbert400m).
+
+## Training
+
+- Library: [sentence-transformers](https://www.sbert.net/)
+- FinBERT model: TurkuNLP/bert-base-finnish-uncased-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 [HuggingFace documentation](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-uncased-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-uncased-finnish-paraphrase')
+model = AutoModel.from_pretrained('TurkuNLP/sbert-uncased-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': True}) 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.

added_tokens.json

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config.json

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+  "model_type": "bert",
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config_sentence_transformers.json

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

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sentence_bert_config.json

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+  "do_lower_case": true
+}

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