SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
1from sentence_transformers import SentenceTransformer
23# Download from the 🤗 Hub4model = SentenceTransformer("Snivellus789/router-embedding-tuned")5# Run inference6sentences =[7'In Swift, what function can I use to shorten the sentence "I\'m feeling kind of tired after having worked all day" while maintaining the same meaning and tone? Can you provide an example of the shortened sentence using the function? ',8'Convert the given XML code to JSON code. <root>\n <data>\n <item id="1">\n <name>Sample data</name>\n <type>Text</type>\n <value>123</value>\n </item>\n </data>\n</root>',9'How can I create a C# program that generates a travel itinerary based on user preferences and available destinations? The program should take into account factors such as budget, time of year, and desired activities (such as hiking or sightseeing). Please use the following data format to represent the available destinations:\n```csharp\nList<Destination> destinations = new List<Destination>\n{\n new Destination\n {\n Name = "Paris",\n Country = "France",\n Activities = new List<string> {"sightseeing", "shopping", "museums"},\n Cost = 5000,\n Season = "spring"\n },\n new Destination\n {\n Name = "Tokyo",\n Country = "Japan",\n Activities = new List<string> {"sightseeing", "food", "temples"},\n Cost = 8000,\n Season = "fall"\n },\n new Destination\n {\n Name = "Sydney",\n Country = "Australia",\n Activities = new List<string> {"beaches", "hiking", "wildlife"},\n Cost = 7000,\n Season = "summer"\n },\n new Destination\n {\n Name = "Marrakesh",\n Country = "Morocco",\n Activities = new List<string> {"sightseeing", "shopping", "food"},\n Cost = 4000,\n Season = "winter"\n }\n};\npublic class Destination\n{\n public string Name { get; set; }\n public string Country { get; set; }\n public List<string> Activities { get; set; }\n public int Cost { get; set; }\n public string Season { get; set; }\n}\n```\nPlease provide step-by-step instructions for using the program and any necessary inputs. ',10]11embeddings = model.encode(sentences)12print(embeddings.shape)13# [3, 384]1415# Get the similarity scores for the embeddings16similarities = model.similarity(embeddings, embeddings)17print(similarities.shape)18# [3, 3]
Training Details
Training Dataset
Unnamed Dataset
Size: 1,000 training samples
Columns: sentence and label
Approximate statistics based on the first 1000 samples:
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}
BatchAllTripletLoss
bibtex
1@misc{hermans2017defense,
2 title={In Defense of the Triplet Loss for Person Re-Identification},
3 author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
4 year={2017},
5 eprint={1703.07737},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV}
8}
