NeuML
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pubmedbert-base-embeddings-1M

Sentence Similarity

sentence-transformers

feature-extraction

static-embeddings

Inference Endpoints

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davidmezzetti commited on 22 days ago

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@@ -111,7 +111,7 @@ embeddings.index(tqdm(ds["abstract"]))
 | -------------------------------------------------------------------------------------- | ----------      | -------------- |
 | [pubmedbert-base-embeddings-100K](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | 0.2             | 19             |
 | [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 1.0             | 17             |
-| **[pubmedbert-base-embeddings-1M](https://hf.co/neuml/pubmedbert-base-embeddings-1M)** | **2.0**         | 17             |
 | [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M)     | 7.5             | 17             |
 Vocabulary pruning doesn't change the runtime performance in this case. But the model is much smaller. Vectors are stored at `int16` precision. This can be beneficial to smaller/lower powered embedded devices and could lead to faster vectorization times.

 | -------------------------------------------------------------------------------------- | ----------      | -------------- |
 | [pubmedbert-base-embeddings-100K](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | 0.2             | 19             |
 | [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 1.0             | 17             |
+| [**pubmedbert-base-embeddings-1M**](https://hf.co/neuml/pubmedbert-base-embeddings-1M) | **2.0**         | 17             |
 | [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M)     | 7.5             | 17             |
 Vocabulary pruning doesn't change the runtime performance in this case. But the model is much smaller. Vectors are stored at `int16` precision. This can be beneficial to smaller/lower powered embedded devices and could lead to faster vectorization times.