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Add prefix to examples in model_doc rst (#11226)

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* Add prefix to examples in model_doc rst

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Yusuke Mori
2021-04-14 23:58:55 +09:00
committed by GitHub
parent 4670b57ce9
commit 075e821d1d
4 changed files with 56 additions and 56 deletions
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20
docs/source/model_doc/herbert.rst
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@@ -40,20 +40,20 @@ Examples of use:
.. code-block::
from transformers import HerbertTokenizer, RobertaModel
>>> from transformers import HerbertTokenizer, RobertaModel
tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
>>> tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
>>> model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
outputs = model(encoded_input)
>>> encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
>>> outputs = model(encoded_input)
# HerBERT can also be loaded using AutoTokenizer and AutoModel:
import torch
from transformers import AutoModel, AutoTokenizer
>>> # HerBERT can also be loaded using AutoTokenizer and AutoModel:
>>> import torch
>>> from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
>>> tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
>>> model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
The original code can be found `here <https://github.com/allegro/HerBERT>`__.