Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use onelevelstudio/M-MPNET-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use onelevelstudio/M-MPNET-BASE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("onelevelstudio/M-MPNET-BASE") 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] - Transformers
How to use onelevelstudio/M-MPNET-BASE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("onelevelstudio/M-MPNET-BASE") model = AutoModel.from_pretrained("onelevelstudio/M-MPNET-BASE") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -79,8 +79,6 @@ This is just a backup for [sentence-transformers/paraphrase-multilingual-mpnet-b
|
|
| 79 |
|
| 80 |
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
| 81 |
|
| 82 |
-
|
| 83 |
-
|
| 84 |
## Usage (Sentence-Transformers)
|
| 85 |
|
| 86 |
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
|
@@ -138,8 +136,6 @@ print("Sentence embeddings:")
|
|
| 138 |
print(sentence_embeddings)
|
| 139 |
```
|
| 140 |
|
| 141 |
-
|
| 142 |
-
|
| 143 |
## Full Model Architecture
|
| 144 |
```
|
| 145 |
SentenceTransformer(
|
|
|
|
| 79 |
|
| 80 |
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
| 81 |
|
|
|
|
|
|
|
| 82 |
## Usage (Sentence-Transformers)
|
| 83 |
|
| 84 |
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
|
|
|
| 136 |
print(sentence_embeddings)
|
| 137 |
```
|
| 138 |
|
|
|
|
|
|
|
| 139 |
## Full Model Architecture
|
| 140 |
```
|
| 141 |
SentenceTransformer(
|