Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert_hash
feature-extraction
custom_code
Instructions to use NeuML/biomedbert-hash-nano-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/biomedbert-hash-nano-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/biomedbert-hash-nano-embeddings", 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] - Transformers
How to use NeuML/biomedbert-hash-nano-embeddings with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/biomedbert-hash-nano-embeddings", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -107,7 +107,7 @@ Evaluation results are shown below. The [Pearson correlation coefficient](https:
|
|
| 107 |
| [bioclinical-modernbert-base-embeddings](https://hf.co/neuml/bioclinical-modernbert-base-embeddings) | 92.49 | 97.10 | 97.04 | 95.54 |
|
| 108 |
| [biomedbert-base-colbert](https://hf.co/neuml/biomedbert-base-colbert) | 94.59 | 97.18 | 96.21 | 95.99 |
|
| 109 |
| [biomedbert-base-reranker](https://hf.co/neuml/biomedbert-base-reranker) | 97.66 | 99.76 | 98.81 | 98.74 |
|
| 110 |
-
| [biomedbert-hash-nano-colbert](https://hf.co/neuml/biomedbert-
|
| 111 |
| [**biomedbert-hash-nano-embeddings**](https://hf.co/neuml/biomedbert-hash-nano-embeddings) | **90.39** | **96.29** | **95.32** | **94.00** |
|
| 112 |
| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings) | 93.27 | 97.00 | 96.58 | 95.62 |
|
| 113 |
| [pubmedbert-base-embeddings-8M](https://hf.co/neuml/pubmedbert-base-embeddings-8M) | 90.05 | 94.29 | 94.15 | 92.83 |
|
|
|
|
| 107 |
| [bioclinical-modernbert-base-embeddings](https://hf.co/neuml/bioclinical-modernbert-base-embeddings) | 92.49 | 97.10 | 97.04 | 95.54 |
|
| 108 |
| [biomedbert-base-colbert](https://hf.co/neuml/biomedbert-base-colbert) | 94.59 | 97.18 | 96.21 | 95.99 |
|
| 109 |
| [biomedbert-base-reranker](https://hf.co/neuml/biomedbert-base-reranker) | 97.66 | 99.76 | 98.81 | 98.74 |
|
| 110 |
+
| [biomedbert-hash-nano-colbert](https://hf.co/neuml/biomedbert-hash-nano-colbert) | 90.45 | 96.81 |92.00 | 93.09 |
|
| 111 |
| [**biomedbert-hash-nano-embeddings**](https://hf.co/neuml/biomedbert-hash-nano-embeddings) | **90.39** | **96.29** | **95.32** | **94.00** |
|
| 112 |
| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings) | 93.27 | 97.00 | 96.58 | 95.62 |
|
| 113 |
| [pubmedbert-base-embeddings-8M](https://hf.co/neuml/pubmedbert-base-embeddings-8M) | 90.05 | 94.29 | 94.15 | 92.83 |
|