Instructions to use glossia/Rambley-V2.2-64M-Embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use glossia/Rambley-V2.2-64M-Embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("glossia/Rambley-V2.2-64M-Embed", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -19,7 +19,7 @@ datasets:
|
|
| 19 |
|
| 20 |
[](https://discord.gg/6acXmq3yx2)
|
| 21 |
|
| 22 |
-
Finetuned version of [glossia/Rambley-V2.
|
| 23 |
|
| 24 |
## Architecture
|
| 25 |
|
|
@@ -31,7 +31,9 @@ Finetuned version of [glossia/Rambley-V2.1-64M](https://hf.co/glossia/Rambley-V2
|
|
| 31 |
|
| 32 |
## Benchmarks
|
| 33 |
|
| 34 |
-
|
|
|
|
|
|
|
| 35 |
|
| 36 |
## Usage
|
| 37 |
|
|
|
|
| 19 |
|
| 20 |
[](https://discord.gg/6acXmq3yx2)
|
| 21 |
|
| 22 |
+
Finetuned version of [glossia/Rambley-V2.2-64M](https://hf.co/glossia/Rambley-V2.2-64M) for embedding purposes on a subset of the nomic-embed training data.
|
| 23 |
|
| 24 |
## Architecture
|
| 25 |
|
|
|
|
| 31 |
|
| 32 |
## Benchmarks
|
| 33 |
|
| 34 |
+
WIP
|
| 35 |
+
|
| 36 |
+
Detailed logs in [glossia/Rambley-V2.2-64M-Embed-mteb-results](https://huggingface.co/datasets/glossia/Rambley-V2.2-64M-Embed-mteb-results)
|
| 37 |
|
| 38 |
## Usage
|
| 39 |
|