* chore: Remove typing hints of Python less than 3.9
* chore: Removed optional from cache as it cannot be undefined
* improve: Turned off progress bar of huggingface models if cached
* feat: Added jina reranker models
* chore: Added jina reranker canonical score values
* chore: added rounding of the output for easier reproducability
* chore: Added jina reranker models in batch test
* chore: remove redundant np.round
* chore: test only <1gb files in local
* chore: Updated docs to add rerankers
* fix: recompute canonical values with fp16
* new: extend NOTICE with jina reranker v2
---------
Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
* Add rerankers to the list of supported models (Supported_Models.ipynb)
* fix: update imports
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Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
* feat: Added support for jina-colbert-v2
* chore: Generalized query marker and document marker
* nit: remove github action on dispatch
* chore: updated license
* fix: Fix attention mask to be all 1 in xlmrobertatokenizer
* feat: Added class for JinaColbertV2
* feat: Added jina colbert
* chore: Change tolerance of the test
* chore: Changed encoding of attention mask to 1 to be only in queries
* chore: Changed the replacable token to be ' @' as its considered as one token
* chore: Removed redundant functions
* chore: Updated supported models docs
* nit: Remove print statement
* nit: visual stuff
* fix: Fix dimention of jina colbert in description
* fix: canonical query and document values for jina colbert
* chore: Updating docs of installing fastembed-gpu
* chore: updated common issues
* chore: Update gcp setup
* chore: Updated an example of installing cuda12 and cudnn9 on ubuntu 22.04
* rephrasing
* update readme
---------
Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
* Re-run of identical hardware and generate graphs
* Re-run of identical hardware and generate graphs
Fixes https://github.com/qdrant/fastembed/issues/174
* Change dataset source
* Refactor code for better readability and maintainability
* Inline outputs
* Replace hard coded constants with dataset specific n_dim
* fix: fix binary quant from scratch notebook
* fix: fix result table, explain corner case
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Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
* Add SPLADE v1
* WIP SPLADE Export errors
* add ONNX model to HF hub and use that
* Update sentences in Converting_SPLADE_to_ONNX.ipynb
* Remove unnecessary files and directories
* Rename var in TextEmbedding class to use EMBEDDING_MODEL_TYPE
* Add SPLADE to list of text embeddings
* Add SPLADE model support for text embedding
* Fix deprecation warning in embedding.py
* Add test for batch embedding with sparse embeddings
* Refactor import statement in test_sparse_embeddings.py
* Rename nbs
* Update vocab size in SPLADE model
* Fix canonical vector lookup in test_text_onnx_embeddings.py
* review refactoring
* restore list_supported_models in OnnxTextEmbedding
* Remove unused method _preprocess_onnx_input() in SpladePP class
* Update SPLADE_PP_en_v1 source in splade_pp.py
* Refactor onnx_model.py to change base model behavior
* extend tests to sparse values as well as indicies
* chore: pre-commit hooks
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Co-authored-by: generall <andrey@vasnetsov.com>
Co-authored-by: Anush008 <anushshetty90@gmail.com>
* Update FastEmbed README.md
* Rewrite GettingStarted to use TextEmbedding instead of DefaultEmbedding
* Improve grammar
* Update Getting Started.ipynb with model information and document format
* Update version from 0.2.0 (yanked) to 0.2.1
* Update text embedding to include prefix for passages and queries
* Update supported models to use the latest API
* * fix(text_embedding_base.py): remove unnecessary prefix from texts in embed method
* feat(text_embedding_base.py): update query_embed method to updated instruction for the v1.5 model
* Remove comparison, since the ranking is identical even with varying embedding
* Refactor text embedding query handling
* fix: query in text_embedding_base to work with both Iterable and str as users might supply both
* Fix Qdrant query to align with future usage
* * refactor(text_embedding_base.py): change query parameter type from str to Union[str, Iterable[str]] in query_embed method
* Update return type of query_embed method
* Update return type in TextEmbeddingBase
* * feat(Supported_Models.ipynb): add support for BAAI/bge-small-zh-v1.5 model
* feat(Supported_Models.ipynb): add support for jinaai/jina-embeddings-v2-base-en model
* feat(Supported_Models.ip
* * chore(docs): update Getting Started.ipynb with progressbar
