dbourget
/

philai-embeddings-v1.1

Sentence Similarity

sentence-transformers

feature-extraction

text-embeddings-inference

Inference Endpoints

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dbourget commited on May 23

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 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-The model is based on BERT-large-uncased, further trained using the TSDAE technique described [here](https://sbert.net/examples/unsupervised_learning/TSDAE/README.html) on a corpus of open access philosophy from [https://philpapers.org](PhilArchive.org).
 The aim was to make an embedding model that can correctly captures semantic similarity between sentences that use philosophical jargon. The aim was partly achieved. In our informal benchmarks, it performs better than Ada3-large. However, it is still far from perfect and we hope to release an improved model soon.

 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+The model is based on BERT-large-uncased, further trained using the TSDAE technique described [here](https://sbert.net/examples/unsupervised_learning/TSDAE/README.html) on a corpus of open access philosophy from [PhilArchive.org](https://philarchive.org).
 The aim was to make an embedding model that can correctly captures semantic similarity between sentences that use philosophical jargon. The aim was partly achieved. In our informal benchmarks, it performs better than Ada3-large. However, it is still far from perfect and we hope to release an improved model soon.