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
Download eval/selection.json from junaid008/hindko-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/junaid008/hindko-embeddings/resolve/main/eval/selection.json
- Command line
-
hf download hf://junaid008/hindko-embeddings/eval/selection.json
-
curl -L -o selection.json https://huggingface.co/junaid008/hindko-embeddings/resolve/main/eval/selection.json
1.66 kB
| { | |
| "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." | |
| } |