Update README.md

README.md

@@ -1,19 +1,31 @@
 ---
+language: 
+- sk
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-- transformers
+license: cc
+datasets:
+- glue
+metrics:
+- spearmanr
+widget:
+- text: "Kde tá ľudská duša drieme?"
 ---
 
-# kinit/slovakbert-sts-stsb
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+# Sentence similarity model based on SlovakBERT
 
-<!--- Describe your model here -->
+This is a sentence similarity model based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model was fine-tuned using [STSbenchmark](ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) [Cer et al 2017] translated to Slovak using [M2M100](https://huggingface.co/facebook/m2m100_1.2B). The model can be used as an universal sentence encoder for Slovak sentences.
 
-## Usage (Sentence-Transformers)
+## Results
+
+The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuliak et al 2021, Section 4.3]. It achieves \\(0.791\%\\) Spearman correlation.
+
+
+## Usage
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
@@ -33,61 +45,26 @@ print(embeddings)
 ```
 
 
+## Cite
 
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```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 = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('kinit/slovakbert-sts-stsb')
-model = AutoModel.from_pretrained('kinit/slovakbert-sts-stsb')
-
-# 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
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=kinit/slovakbert-sts-stsb)
-
-
-
-## Full Model Architecture
 ```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
-  (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})
-)
+@article{DBLP:journals/corr/abs-2109-15254,
+  author    = {Mat{\'{u}}s Pikuliak and
+               Stefan Grivalsky and
+               Martin Konopka and
+               Miroslav Blst{\'{a}}k and
+               Martin Tamajka and
+               Viktor Bachrat{\'{y}} and
+               Mari{\'{a}}n Simko and
+               Pavol Bal{\'{a}}zik and
+               Michal Trnka and
+               Filip Uhl{\'{a}}rik},
+  title     = {SlovakBERT: Slovak Masked Language Model},
+  journal   = {CoRR},
+  volume    = {abs/2109.15254},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2109.15254},
+  eprinttype = {arXiv},
+  eprint    = {2109.15254},
+}
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->