Comments (4)
Try:
def _get_entity_spans(
model,
input_sentences,
prefix_allowed_tokens_fn,
redirections=None,
):
output_sentences = model.sample(
input_sentences,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
)
output_sentences = [e[0]["text"] for e in output_sentences]
return get_entity_spans_finalize(
input_sentences, output_sentences, redirections=redirections
)
from genre.
It works perfectly thank you. I just added an import of from genre.utils import get_entity_spans_finalize
.
from genre.
get_entity_spans
first calls a pre-processing step on the inputs:
Line 97 in 3ac9343
Line 111 in 3ac9343
from genre.
@nicola-decao thank you. I would like that get_entity_spans
had the same output of model.sample
. Currently, the output of the latter (model.sample
) seems to me more accurate than the former. So how to "force" get_entity_spans
to have the same result of model.sample
?
Examples
sentences = ["Tired of the lies? Tired of the spin? Are you ready to hear the hard-hitting truth in comprehensive, conservative, principled fashion? The Ben Shapiro Show brings you all the news you need to know in the most fast moving daily program in America. Ben brutally breaks down the culture and never gives an inch! Monday thru Friday."]
# model.sample
prefix_allowed_tokens_fn = get_prefix_allowed_tokens_fn(model, sentences)
out = model.sample(
sentences,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
)
print(out)
# get_entity_spans
entity_spans = get_entity_spans( model, sentences)
print(get_markdown(sentences, entity_spans))
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
- 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.