BERT tiny trained on GooAQ This is a Cross Encoder model finetuned from prajjwal1/bert tiny using the sentence transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using train script.py. Model Details Model Description Model Type: Cross Encoder Base model: prajjwal1/bert tiny Maximum Sequence Length: 512 tokens Number of Output Labels: 1 label Language: en License: apache 2.0 Model Sources Documentation: Sentence Transformers Documentation Documentation: Cross Encoder Documentation Repository: Sentence Transformers on GitHub Hugging Face: Cross Encoders on Hugging Face 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 Cross Encoder Reranking Datasets: gooaq dev , NanoMSMARCO , NanoNFCorpus and NanoNQ Evaluated with CrossEncoderRerankingEvaluator Metric gooaq dev NanoMSMARCO NanoNFCorpus NanoNQ : : : : : map 0.5677 (+0.0366) 0.4280 ( 0.0616) 0.3397 (+0.0787…
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