update readme

README.txt

@@ -1,21 +1,36 @@
+---
+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
+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
 
-    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/)
+- 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`
+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."]
 
@@ -26,12 +41,12 @@ print(embeddings)
 
 ### HuggingFace Transformers
 
-```
+```python
 from transformers import AutoTokenizer, AutoModel
 import torch
 
 
-#Mean Pooling - Take attention mask into account for correct averaging
+# 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()
@@ -58,3 +73,25 @@ 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.