Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use NeuML/pubmedbert-base-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-embeddings") 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/pubmedbert-base-embeddings with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NeuML/pubmedbert-base-embeddings") model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 736901c
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README.md
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@@ -97,7 +97,7 @@ Evaluation results are shown below. The [Pearson correlation coefficient](https:
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| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.86 | 94.07 | 93.44 |
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| [bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) | 91.02 | 95.60 | 94.49 | 93.70 |
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| [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.83 | 96.24 | 95.35 |
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| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.33 | 93.54 | 92.58 |
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## Training
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| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.86 | 94.07 | 93.44 |
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| [bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) | 91.02 | 95.60 | 94.49 | 93.70 |
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| [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.83 | 96.24 | 95.35 |
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| [**pubmedbert-base-embeddings**](https://hf.co/neuml/pubmedbert-base-embeddings) | **93.27** | **97.07** | **96.58** | **95.64** |
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| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.33 | 93.54 | 92.58 |
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## Training
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