Sentence Similarity
sentence-transformers
Safetensors
Northern Hindko
Southern Hindko
xlm-roberta
feature-extraction
hindko
embeddings
low-resource
urdu-script
perso-arabic
Eval Results (legacy)
text-embeddings-inference
Instructions to use junaid008/hindko-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use junaid008/hindko-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("junaid008/hindko-embeddings") 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: 1,663 Bytes
4734758 | 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 | {
"rule": "pool = round-2 dev results + incumbents ft-e5-base (size 2) and ft-e5-small (size 1) with their round-1 dev scores; best = max overall; near = overall >= best - 0.003; selected = smallest size rank in near (ties by higher score); runner-up = best-scoring other model",
"dev_scores": {
"ft-e5-base": 0.6851297501603847,
"ft-e5-small": 0.6700276166417629,
"r2-bgem3": 0.7007568407810918,
"r2-e5b": 0.7079351835925609,
"r2-e5b-plain": 0.7066095544829916,
"r2-e5l": 0.7117165057564516,
"r2-e5s": 0.7038754488016046
},
"near": [
"r2-e5l"
],
"selected": "r2-e5l",
"runner_up": "r2-e5b",
"tie": 0.003,
"written_utc": "2026-10-01T17:48:17Z",
"gate": {
"pass": true,
"decided_utc": "2026-10-01T15:38:50Z",
"BookTitleRetrieval": 0.446437329167755,
"overall": 0.7079351835925609,
"BookClustering": 0.7339101300956627
},
"pairs_sha256": {
"rows.jsonl": "9ddde8f6ef2750166e4d895b8aba55ba394ba954961169ab0aec0f5d4a541faa",
"texts.jsonl": "f0c31d84f79e7c3cee0b45e708ec1bb1929f9e2d99d7fbdec66ecb47a1024a28"
},
"size_rank": {
"r2-e5s": 1,
"ft-e5-small": 1,
"r2-e5b": 2,
"r2-e5b-plain": 2,
"ft-e5-base": 2,
"r2-e5l": 3,
"r2-bgem3": 4
},
"notes": "dev_scores of ft-e5-base and ft-e5-small are their round-1 dev results (max_seq_length 256, float32); all other pool members were evaluated at max_seq_length 512 in float16. Test was run once, after this selection, on the selected model and the runner-up. gate = the pre-registered recipe gate evaluated on r2-e5b dev (BookTitleRetrieval >= 0.36, overall >= 0.685, BookClustering >= 0.68); pass -> the large runs used mined hard negatives and same-book blocks."
} |