Comments (3)
Hi! It seems you are using the wrong checkpoints. The e2e
models are for the end-to-end linking task when instead you are evaluating for named entity disambiguation.
from genre.
Hello,
thanks for your response. I understand better why the results are different.
Do you have the possibility to share the evaluation script on entity linking too ?
from genre.
Unfortunately no, I do not work at FAIR anymore and the script is not public. You would need to write your own. However, the models are public.
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 HOT 1
- 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
- format of entries for entity linking training HOT 2
- 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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from genre.