Sentence Similarity
sentence-transformers
Safetensors
bert
embeddings
cross-lingual
multilingual
igbo
hausa
yoruba
information-retrieval
semantic-search
text-embeddings-inference
Instructions to use Modularcomputing/Native-Bird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Modularcomputing/Native-Bird with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Modularcomputing/Native-Bird") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 897 Bytes
b935288 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | {
"baseline": {
"ig/clean": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"ig/asr_noise": {
"R@1": 0.9853,
"R@10": 0.9951,
"MRR": 0.9888
},
"ha/clean": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"ha/asr_noise": {
"R@1": 0.9951,
"R@10": 1.0,
"MRR": 0.9975
},
"yo/clean": {
"R@1": 0.9804,
"R@10": 1.0,
"MRR": 0.9871
},
"yo/asr_noise": {
"R@1": 0.9559,
"R@10": 0.9951,
"MRR": 0.9683
}
},
"finetuned": {
"ig/clean": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"ig/asr_noise": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"ha/clean": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"ha/asr_noise": {
"R@1": 1.0,
"R@10": 1.0,
"MRR": 1.0
},
"yo/clean": {
"R@1": 0.9804,
"R@10": 1.0,
"MRR": 0.9902
},
"yo/asr_noise": {
"R@1": 0.9804,
"R@10": 1.0,
"MRR": 0.9867
}
}
} |