Integration of dense embedding model Snowflake arctic-embed-m-v1.5 #2001
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This is linked to resolving issue #1959 |
lintool
requested changes
Oct 1, 2024
pyserini/search/faiss/_searcher.py
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| @@ -362,6 +364,43 @@ def encode(self, query: str, **kwargs): | |||
| return self.get_embedding(inputs) | |||
| else: | |||
| return super().encode(query) | |||
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| class ArcticQueryEncoder(QueryEncoder): | |||
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Why are there two different ArcticQueryEncoder classes? Here and in encode._artic?
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Yeah that is how the current version of pyserini does for all query encoders. Shivani and I were talking about this too. Maybe we could make a separate issue on this matter?
pyserini/search/faiss/_searcher.py
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| @@ -413,6 +452,14 @@ def encode(self, query: str): | |||
| return embeddings.flatten() | |||
| else: | |||
| return super().encode(query) | |||
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lintool
requested changes
Oct 14, 2024
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lg, but can you please add test cases, like in: https://github.com/castorini/pyserini/blob/master/tests/test_encoder_model_dpr.py
So maybe test case for the document encoder first - add query encoder test cases when we merge in the BEIR regressions?
lintool
approved these changes
Oct 15, 2024
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This update integrates the Snowflake arctic-embed-m-v1.5 dense embedding model into the Pyserini toolkit. A new class was developed to support the entire snowflake-series of models, offering a scalable foundation for future expansions, such as additional Snowflake embedding model integrations. This modification is intended to lay the groundwork for smoother integration of future models from the Snowflake series