SPLADE BERT tiny trained on Natural Questions tuples This is a SPLADE Sparse Encoder model trained on the natural questions dataset using the sentence transformers library. It maps sentences & paragraphs to a 30522 dimensional sparse vector space and can be used for semantic search and sparse retrieval. This model was trained using train script.py. Model Details Model Description Model Type: SPLADE Sparse Encoder Maximum Sequence Length: 512 tokens Output Dimensionality: 30522 dimensions Similarity Function: Dot Product Training Dataset: natural questions Language: en License: apache 2.0 Model Sources Documentation: Sentence Transformers Documentation Documentation: Sparse Encoder Documentation Repository: Sentence Transformers on GitHub Hugging Face: Sparse Encoders on Hugging Face Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Click to see the direct usage in Transformers Click to expand Evaluation Metrics Sparse Information Retrieval Datasets: NanoMSMARCO , NanoNFCorpus and NanoNQ Evaluated with SparseInformationRetrievalEvaluator Metric NanoMSMARCO NanoNFCorpus N…
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