Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
dense-encoder
dense
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Rune-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Rune-Preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("telepix/PIXIE-Rune-Preview") 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -57,7 +57,7 @@ Our model, **telepix/PIXIE-Rune-Preview**, achieves strong performance across mo
|
|
| 57 |
|
| 58 |
| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|
| 59 |
|------|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 60 |
-
| **telepix/PIXIE-Rune-Preview** |
|
| 61 |
| telepix/PIXIE-Splade-Preview | 0.1B | 0.6677 | 0.6238 | 0.6628 | 0.6831 | 0.7009 |
|
| 62 |
| | | | | | | |
|
| 63 |
| nlpai-lab/KURE-v1 | 0.5B | 0.6751 | 0.6277 | 0.6725 | 0.6907 | 0.7095 |
|
|
@@ -89,15 +89,14 @@ Our model, **telepix/PIXIE-Rune-Preview**, achieves strong performance on a wide
|
|
| 89 |
|
| 90 |
| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|
| 91 |
|------|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 92 |
-
| **telepix/PIXIE-Rune-Preview** |
|
| 93 |
| | | | | | | |
|
| 94 |
-
| Snowflake/snowflake-arctic-embed-l-v2.0 |
|
| 95 |
-
| Qwen/Qwen3-Embedding-0.6B |
|
| 96 |
-
| Alibaba-NLP/gte-multilingual-base |
|
| 97 |
-
| BAAI/bge-m3 |
|
| 98 |
-
|
|
| 99 |
-
|
|
| 100 |
-
| jinaai/jina-embeddings-v3 | 572M | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
|
| 101 |
|
| 102 |
Descriptions of the benchmark datasets used for evaluation are as follows:
|
| 103 |
- **ArguAna**
|
|
|
|
| 57 |
|
| 58 |
| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|
| 59 |
|------|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 60 |
+
| **telepix/PIXIE-Rune-Preview** | 0.5B | **0.6905** | **0.6461** | **0.6859** | **0.7063** | **0.7238** |
|
| 61 |
| telepix/PIXIE-Splade-Preview | 0.1B | 0.6677 | 0.6238 | 0.6628 | 0.6831 | 0.7009 |
|
| 62 |
| | | | | | | |
|
| 63 |
| nlpai-lab/KURE-v1 | 0.5B | 0.6751 | 0.6277 | 0.6725 | 0.6907 | 0.7095 |
|
|
|
|
| 89 |
|
| 90 |
| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|
| 91 |
|------|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 92 |
+
| **telepix/PIXIE-Rune-Preview** | 0.5B | **0.5781** | **0.5691** | **0.5663** | **0.5791** | **0.5979** |
|
| 93 |
| | | | | | | |
|
| 94 |
+
| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.5812 | 0.5725 | 0.5705 | 0.5811 | 0.6006 |
|
| 95 |
+
| Qwen/Qwen3-Embedding-0.6B | 0.6B | 0.5558 | 0.5321 | 0.5451 | 0.5620 | 0.5839 |
|
| 96 |
+
| Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
|
| 97 |
+
| BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
|
| 98 |
+
| nlpai-lab/KURE-v1 | 0.5B | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
|
| 99 |
+
| jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
|
|
|
|
| 100 |
|
| 101 |
Descriptions of the benchmark datasets used for evaluation are as follows:
|
| 102 |
- **ArguAna**
|