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Update all references to canonical models (#29001)

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This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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docs/source/ko/quicktour.md
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@@ -81,7 +81,7 @@ pip install tensorflow
>>> classifier = pipeline("sentiment-analysis")
```
[`pipeline`]은 감정 분석을 위한 [사전 훈련된 모델](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)과 토크나이저를 자동으로 다운로드하고 캐시합니다. 이제 `classifier`를 대상 텍스트에 사용할 수 있습니다:
[`pipeline`]은 감정 분석을 위한 [사전 훈련된 모델](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english)과 토크나이저를 자동으로 다운로드하고 캐시합니다. 이제 `classifier`를 대상 텍스트에 사용할 수 있습니다:
```py
>>> classifier("We are very happy to show you the 🤗 Transformers library.")
@@ -385,7 +385,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py
>>> from transformers import AutoConfig
>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
>>> my_config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased", n_heads=12)
```
<frameworkcontent>
@@ -422,7 +422,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
2. [`TrainingArguments`]는 학습률, 배치 크기, 훈련할 에포크 수와 같은 모델 하이퍼파라미터를 포함합니다. 훈련 인자를 지정하지 않으면 기본값이 사용됩니다:
@@ -444,7 +444,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
4. 데이터셋을 로드하세요:
@@ -516,7 +516,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
2. 토크나이저, 이미지 프로세서, 특징 추출기(feature extractor) 또는 프로세서와 같은 전처리 클래스를 로드하세요:
@@ -524,7 +524,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
3. 데이터셋을 토큰화하는 함수를 생성하세요: