NeuML
/

pubmedbert-base-embeddings-1M

Sentence Similarity

sentence-transformers

feature-extraction

static-embeddings

Model card Files Files and versions Community

davidmezzetti commited on 13 days ago

Commit

2334d8f

·

1 Parent(s): 2b7fed2

Update README

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -74,12 +74,11 @@ print(embeddings)
 The following compares performance of this model against the models previously compared with [PubMedBERT Embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings#evaluation-results). The following datasets were used to evaluate model performance.
-- [PubMed QA](https://huggingface.co/datasets/pubmed_qa)
   - Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
 - [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
   - Split: test, Pair: (title, text)
-  - _Note: The previously used [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new) dataset is no longer available but a similar dataset is used here_
-- [PubMed Summary](https://huggingface.co/datasets/scientific_papers)
   - Subset: pubmed, Split: validation, Pair: (article, abstract)
 The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.

 The following compares performance of this model against the models previously compared with [PubMedBERT Embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings#evaluation-results). The following datasets were used to evaluate model performance.
+- [PubMed QA](https://huggingface.co/datasets/qiaojin/PubMedQA)
   - Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
 - [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
   - Split: test, Pair: (title, text)
+- [PubMed Summary](https://huggingface.co/datasets/armanc/scientific_papers)
   - Subset: pubmed, Split: validation, Pair: (article, abstract)
 The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.