Sentence Similarity
sentence-transformers
Safetensors
English
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use aamohame/hack_ai_embbedding_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aamohame/hack_ai_embbedding_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aamohame/hack_ai_embbedding_model") sentences = [ "A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book on a bench in the park", "The people are standing still on the curb.", "An elderly man sits in a small shop." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28c2fcbaa9138dbc031f21a2347dff18dbd17363626974fb808c4d91eae9d2ae
- Size of remote file:
- 5.52 kB
- SHA256:
- d83a750af5174d057c5ff30ce52f4f89aff9226335ea7aed07a7f835dfa8844c
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