Comments (2)
The training of the EL task is done via a sequence-to-sequence objective. The input text is plain text and the output is a markup language like this example:
Input: In 1921, Einstein received a Nobel Prize.
Output: In 1921, { Einstein } [ Albert Einstein ] received a { Nobel Prize } [ Nobel Prize in Physics ] .
from genre.
Hello
Thanks for your response but my question was more about the construction of a dataset to train GENRE.
In your ED model, there is only 1 mention by entry, which is unusual for EL task (where the entry is 1 plain document) and ED task (because the context between the differents mentions of a document is lost).
I don't know if this choice is a constraint for fairseq or an optimisation of the model's objective.
I would like to know if for the EL task, I have to process 1 mention by entry or if I have to process 1 document by entry.
from genre.
Related Issues (20)
- is prefix_allowed_tokens_fn only working for seq2seq model.generate? HOT 2
- Loading mgenre models is taking 44GB RAM
- Problem in candidate-based generation on GENRE using transformers >= 4.36.0
- the same entity name question
- Inference speed is too slow. Is this problem because of Constrained beam search?
- can not receive different outputs from mGENRE.sample using dropout in train mode and different seeds HOT 2
- can't find ID to title map json file HOT 1
- alignment between candidate and KILT wikipedia data source HOT 4
- Question: Running genre on multiple GPUs HOT 1
- Invalid prediction - no wikipedia entity HOT 10
- Fail to Reproduce the dev score of GENRE Document Retrieval HOT 7
- mGENRE finetuning issue
- Why do you prepend `eos_token_id' to sent_orig HOT 2
- colab script to run GENRE
- NameError: name 'batched_hypos' is not defined (mGENRE) HOT 5
- [Question] Evaluating mGENRE on Mewsli-9
- Fine-tune with hugging face trainer
- import package error
- Chinese entity linking
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