Sentence Similarity
sentence-transformers
Safetensors
phi
feature-extraction
Generated from Trainer
dataset_size:6552
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use dinho1597/phi-2-telecom-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dinho1597/phi-2-telecom-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dinho1597/phi-2-telecom-ft") sentences = [ "What problem can reconfigurable intelligent surfaces mitigate in light fidelity systems?", "The document mentions that blind channel estimation requires a large number of data symbols to improve accuracy, which may not be feasible in practice.", "Empirical evidence suggests that the power decay can even be exponential with distance.", "Reconfigurable intelligent surface-enabled environments can enhance light fidelity coverage by mitigating the dead-zone problem for users at the edge of the cell, improving link quality." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle