Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Eval Results
Instructions to use sentence-transformers/all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2") 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 sentence-transformers/all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = AutoModelForMaskedLM.from_pretrained("sentence-transformers/all-mpnet-base-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
f965a9b
1
Parent(s): 79d71b4
Add MTEB
Browse files
README.md
CHANGED
|
@@ -2364,7 +2364,7 @@ print(sentence_embeddings)
|
|
| 2364 |
|
| 2365 |
## Evaluation Results
|
| 2366 |
|
| 2367 |
-
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-
|
| 2368 |
|
| 2369 |
------
|
| 2370 |
|
|
|
|
| 2364 |
|
| 2365 |
## Evaluation Results
|
| 2366 |
|
| 2367 |
+
For an automated evaluation of this model, see *MTEB*: https://huggingface.co/spaces/mteb/leaderboard or the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L12-v2)
|
| 2368 |
|
| 2369 |
------
|
| 2370 |
|