Comments (2)
@izuna385 Thanks for the questions. The BERT models are pre-trained on Wikipedia. In Sec 5.2.2, we meant that we also use wikipedia to train our bi-encoder model, initialized from pre-trained BERT models. We'll make that point clear in future versions of the paper.
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Looking forward to the next update. Thanks for kind clarification!
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Related Issues (20)
- Use a smaller model to speed up the prediction time HOT 3
- How to generate embeddings for new candidates? HOT 6
- Slightly different scores when using a quantized model
- Poor recall using non-dense FAISS indexes HOT 2
- How to only generate Precision, Recall, and f1 score when benchmarking BLINK HOT 3
- Biencoder with GPU RuntimeError: Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_select HOT 3
- python: symbol lookup error:
- KeyError HOT 7
- A short tutorial on how to train a smaller biencoder model on custom dataset HOT 1
- Entity linking in Wikidata? HOT 3
- Missing `add_special_tokens` in biencoder? HOT 1
- Average length of words in a Wikipedia Entity HOT 1
- AttributeError: 'KeyedVectors' object has no attribute 'key_to_index' HOT 1
- Add truncation for data_process.get_context_representation
- Tutorial on how to train a Crossencoder HOT 1
- Python 3.7 no longer supported by conda HOT 2
- How to get entity type?
- ValueError in faiss_indexer.py Due to Mismatched Tensor Shapes During ELQ Training
- ELQ Wikipedia-trained biencoder checkpoints
- Can Support Chinese?
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