Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
modernbert
feature-extraction
biomedical
systematic-review
relevance-screening
information-retrieval
pubmed
text-embeddings-inference
Instructions to use Praise2112/siren-screening-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Praise2112/siren-screening-biencoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Praise2112/siren-screening-biencoder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Praise2112/siren-screening-biencoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Praise2112/siren-screening-biencoder") model = AutoModel.from_pretrained("Praise2112/siren-screening-biencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload biencoder model
Browse files
README.md
CHANGED
|
@@ -19,48 +19,6 @@ datasets:
|
|
| 19 |
base_model:
|
| 20 |
- Alibaba-NLP/gte-modernbert-base
|
| 21 |
pipeline_tag: sentence-similarity
|
| 22 |
-
model-index:
|
| 23 |
-
- name: siren-screening-biencoder
|
| 24 |
-
results:
|
| 25 |
-
- task:
|
| 26 |
-
type: information-retrieval
|
| 27 |
-
name: Information Retrieval
|
| 28 |
-
dataset:
|
| 29 |
-
name: SIREN Screening (Internal)
|
| 30 |
-
type: Praise2112/siren-screening
|
| 31 |
-
metrics:
|
| 32 |
-
- type: mrr@10
|
| 33 |
-
value: 0.937
|
| 34 |
-
name: MRR@10
|
| 35 |
-
- type: recall@10
|
| 36 |
-
value: 0.996
|
| 37 |
-
name: Recall@10
|
| 38 |
-
- type: ndcg@10
|
| 39 |
-
value: 0.952
|
| 40 |
-
name: NDCG@10
|
| 41 |
-
- task:
|
| 42 |
-
type: information-retrieval
|
| 43 |
-
name: Information Retrieval
|
| 44 |
-
dataset:
|
| 45 |
-
name: CLEF-TAR 2019
|
| 46 |
-
type: clef-tar-2019
|
| 47 |
-
metrics:
|
| 48 |
-
- type: ndcg@10
|
| 49 |
-
value: 0.434
|
| 50 |
-
name: NDCG@10
|
| 51 |
-
- type: wss@95
|
| 52 |
-
value: 0.931
|
| 53 |
-
name: WSS@95
|
| 54 |
-
- task:
|
| 55 |
-
type: information-retrieval
|
| 56 |
-
name: Information Retrieval
|
| 57 |
-
dataset:
|
| 58 |
-
name: SciFact (BEIR)
|
| 59 |
-
type: BeIR/scifact
|
| 60 |
-
metrics:
|
| 61 |
-
- type: ndcg@10
|
| 62 |
-
value: 0.770
|
| 63 |
-
name: NDCG@10
|
| 64 |
---
|
| 65 |
|
| 66 |
# SIREN Screening Bi-encoder
|
|
|
|
| 19 |
base_model:
|
| 20 |
- Alibaba-NLP/gte-modernbert-base
|
| 21 |
pipeline_tag: sentence-similarity
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 22 |
---
|
| 23 |
|
| 24 |
# SIREN Screening Bi-encoder
|