Update README.md

README.md

@@ -60,4 +60,91 @@ tags:
 - transformers
 ---
 
-This is a F16, Q8_0 GGUF quantisations of base model lang-uk/ukr-paraphrase-multilingual-mpnet-base
+This is a F16, Q8_0 GGUF quantisations of base model lang-uk/ukr-paraphrase-multilingual-mpnet-base
+
+Below is copy of original README.md
+
+# lang-uk/ukr-paraphrase-multilingual-mpnet-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned for Ukrainian language: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+The original model used for fine-tuning is `sentence-transformers/paraphrase-multilingual-mpnet-base-v2`. See our paper [Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation](https://aclanthology.org/2023.unlp-1.2/) for details.
+
+
+## Usage (Sentence-Transformers)
+
+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('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## 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('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
+model = AutoModel.from_pretrained('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
+# 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, average pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Citing & Authors
+
+
+If you find this model helpful, feel free to cite our publication [Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation](https://aclanthology.org/2023.unlp-1.2/):
+```bibtex 
+@inproceedings{laba-etal-2023-contextual,
+    title = "Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation",
+    author = "Laba, Yurii  and
+      Mudryi, Volodymyr  and
+      Chaplynskyi, Dmytro  and
+      Romanyshyn, Mariana  and
+      Dobosevych, Oles",
+    editor = "Romanyshyn, Mariana",
+    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
+    month = may,
+    year = "2023",
+    address = "Dubrovnik, Croatia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.unlp-1.2",
+    doi = "10.18653/v1/2023.unlp-1.2",
+    pages = "11--19",
+    abstract = "This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9{\%} accuracy for lexical meaning prediction for homonyms.",
+}
+```
+
+Copyright: Yurii Laba, Volodymyr Mudryi, Dmytro Chaplynskyi, Mariana Romanyshyn, Oles Dobosevych, [Ukrainian Catholic University](https://ucu.edu.ua/en/), [lang-uk project](https://lang.org.ua/en/), 2023
+
+An original model used for fine-tuning was trained by [sentence-transformers](https://www.sbert.net/).