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add imports to examples (#3160)

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Sam Shleifer
2020-03-06 11:15:33 -05:00
committed by GitHub
parent 6ffe03a0a1
commit e58b3ec5df
1 changed files with 2 additions and 3 deletions
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src/transformers/modeling_bart.py
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@@ -913,7 +913,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
# Mask filling only works for bart-large # Mask filling only works for bart-large
from transformers import BartTokenizer, BartForConditionalGeneration from transformers import BartTokenizer, BartForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained('bart-large') tokenizer = BartTokenizer.from_pretrained('bart-large')
TXT = "My friends are <mask> but they eat too many carbs." TXT = "My friends are <mask> but they eat too many carbs."
model = BartForConditionalGeneration.from_pretrained('bart-large') model = BartForConditionalGeneration.from_pretrained('bart-large')
input_ids = tokenizer.batch_encode_plus([TXT], return_tensors='pt')['input_ids'] input_ids = tokenizer.batch_encode_plus([TXT], return_tensors='pt')['input_ids']
@@ -1031,8 +1031,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
Examples:: Examples::
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
# see ``examples/summarization/bart/evaluate_cnn.py`` for a longer example # see ``examples/summarization/bart/evaluate_cnn.py`` for a longer example
config = BartConfig(vocab_size=50264, output_past=True) # no mask_token_id model = BartForConditionalGeneration.from_pretrained('bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('bart-large-cnn', config=config)
tokenizer = BartTokenizer.from_pretrained('bart-large-cnn') tokenizer = BartTokenizer.from_pretrained('bart-large-cnn')
ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs." ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
inputs = tokenizer.batch_encode_plus([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt') inputs = tokenizer.batch_encode_plus([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')