Instructions to use LilaBoualili/electra-sim-pair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LilaBoualili/electra-sim-pair with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LilaBoualili/electra-sim-pair")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LilaBoualili/electra-sim-pair") model = AutoModelForSequenceClassification.from_pretrained("LilaBoualili/electra-sim-pair") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
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.
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