Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-small") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
a634d9b
1
Parent(s): 64747c1
Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/99) (#4)
Browse files- Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/990) (896066f44115c70dcb90b6ab0c5e7e8ead6bf41a)
Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -2302,13 +2302,13 @@ model-index:
|
|
| 2302 |
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 2303 |
metrics:
|
| 2304 |
- type: cos_sim_pearson
|
| 2305 |
-
value:
|
| 2306 |
- type: cos_sim_spearman
|
| 2307 |
-
value:
|
| 2308 |
- type: dot_pearson
|
| 2309 |
-
value: 24.
|
| 2310 |
- type: dot_spearman
|
| 2311 |
-
value:
|
| 2312 |
- task:
|
| 2313 |
type: Retrieval
|
| 2314 |
dataset:
|
|
|
|
| 2302 |
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 2303 |
metrics:
|
| 2304 |
- type: cos_sim_pearson
|
| 2305 |
+
value: 31.523347880124497
|
| 2306 |
- type: cos_sim_spearman
|
| 2307 |
+
value: 31.388214436391015
|
| 2308 |
- type: dot_pearson
|
| 2309 |
+
value: 24.554034354399012
|
| 2310 |
- type: dot_spearman
|
| 2311 |
+
value: 23.501532108411913
|
| 2312 |
- task:
|
| 2313 |
type: Retrieval
|
| 2314 |
dataset:
|