Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:396
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use arad1367/technographics-marketing-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use arad1367/technographics-marketing-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("arad1367/technographics-marketing-matryoshka") sentences = [ "How can technographics contribute to predicting consumer behavior in digital marketing?", "Data analysis is essential in predicting consumer behavior in digital marketing. Analysis of data related to consumer behavior, preferences and needs can reveal patterns and trends that can be used to forecast future behavior and refine marketing strategies.", "Technographics enables businesses to understand the technological habits and preferences of their customers. By analyzing this data, companies can predict how these users are likely to interact with their digital products or services, and tailor their marketing strategies accordingly.", "The key components include data collection (gathering data from various sources), data analysis (using algorithms and models to analyze data), and predictive modelling (predicting future customer behavior based on analyzed data)." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle