初始化项目，由ModelHub XC社区提供模型

Model: kinit/slovakbert-sts-stsb
Source: Original Platform

README.md

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+---
+language:
+- sk
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- sts
+license: cc-by-4.0
+datasets:
+- glue
+metrics:
+- spearmanr
+widget:
+- source_sentence: Izrael uskutočnil letecké údery v blízkosti Damasku.
+  sentences:
+  - Izrael uskutočnil vzdušný útok na Sýriu.
+  - Pes leží na gauči a má hlavu na bielom vankúši.
+base_model:
+- gerulata/slovakbert
+library_name: sentence-transformers
+---
+
+
+# Sentence similarity model based on SlovakBERT
+
+This is a sentence similarity model based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model was fine-tuned using [STSbenchmark](https://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.
+
+## 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 on STSbenchmark test set.
+
+
+## Usage
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('kinit/slovakbert-sts-stsb')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+## Cite
+
+```
+@inproceedings{pikuliak-etal-2022-slovakbert,
+    title = "{S}lovak{BERT}: {S}lovak Masked Language Model",
+    author = "Pikuliak, Mat{\'u}{\v{s}}  and
+      Grivalsk{\'y}, {\v{S}}tefan  and
+      Kon{\^o}pka, Martin  and
+      Bl{\v{s}}t{\'a}k, Miroslav  and
+      Tamajka, Martin  and
+      Bachrat{\'y}, Viktor  and
+      Simko, Marian  and
+      Bal{\'a}{\v{z}}ik, Pavol  and
+      Trnka, Michal  and
+      Uhl{\'a}rik, Filip",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.530",
+    pages = "7156--7168",
+    abstract = "We introduce a new Slovak masked language model called \textit{SlovakBERT}. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.",
+}
+```