Text Classification
sentence-transformers
Safetensors
English
Chinese
xlm-roberta
mteb
text-embeddings-inference
Eval Results (legacy)
Instructions to use Anas989898/creator-reranker-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Anas989898/creator-reranker-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Anas989898/creator-reranker-v1") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Download eval/CrossEncoderCorrelationEvaluator_creator-val_results.csv from Anas989898/creator-reranker-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/Anas989898/creator-reranker-v1/resolve/main/eval/CrossEncoderCorrelationEvaluator_creator-val_results.csv
- Command line
-
hf download hf://Anas989898/creator-reranker-v1/eval/CrossEncoderCorrelationEvaluator_creator-val_results.csv
-
curl -L -o CrossEncoderCorrelationEvaluator_creator-val_results.csv https://huggingface.co/Anas989898/creator-reranker-v1/resolve/main/eval/CrossEncoderCorrelationEvaluator_creator-val_results.csv
1.02 kB
| epoch,steps,Pearson_Correlation,Spearman_Correlation | |
| 1.0,172,0.7949616603149585,0.7569540697669113 | |
| 2.0,344,0.8577981587729625,0.8365687911307925 | |
| 3.0,516,0.877040143043997,0.8494517359120413 | |
| 4.0,688,0.8734229499262265,0.8598879258272372 | |
| 5.0,860,0.8730185908242762,0.8548730676058177 | |
| 6.0,1032,0.8885178285635745,0.8665093325267443 | |
| 7.0,1204,0.9005301321419754,0.8771055617539337 | |
| 8.0,1376,0.8847242524252361,0.8642349276889919 | |
| 9.0,1548,0.8742336479526381,0.8555712221844758 | |
| 10.0,1720,0.8999831579113904,0.878164122250963 | |
| 11.0,1892,0.901630621081326,0.8764939959170371 | |
| 12.0,2064,0.8968061080163883,0.8725952174393348 | |
| 13.0,2236,0.8967140388246322,0.8738119321991269 | |
| 14.0,2408,0.8981688436161146,0.8729157145463482 | |
| 15.0,2580,0.8937604374139216,0.8736111106454841 | |
| 16.0,2752,0.8975364114728186,0.8744185571194216 | |
| 17.0,2924,0.8995046146008265,0.8754525706150343 | |
| 18.0,3096,0.8948750462283641,0.8740643796933018 | |
| 19.0,3268,0.8964012572087307,0.8739010069781944 | |
| 20.0,3440,0.8974188044296665,0.874827972465985 | |