At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassification classes but it follows the same classification layer defined for BERT similarly to the TFElectraRelevanceHead in the Capreolus BERT-MaxP implementation.

Refer to our [github repository](https://github.com/BOUALILILila/ExactMatchMarking) for a usage example for ad hoc ranking.
{}
transformers pytorch tf electra text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 

At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassification classes but it follows the same classification layer defined for BERT similarly to the TFElectraRelevanceHead in the Capreolus BERT-MaxP implementation.

Refer to our github repository for a usage example for ad hoc ranking.
TAGS
#transformers #pytorch #tf #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #tf #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 
