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chenpangpang
transformers
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cdf19c50
Unverified
Commit
cdf19c50
authored
Feb 15, 2022
by
Nicolas Patry
Committed by
GitHub
Feb 15, 2022
Browse files
Re-export `KeyDataset`. (#15645)
* Re-export `KeyDataset`. * Update the docs locations.
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28e6155d
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docs/source/main_classes/pipelines.mdx
docs/source/main_classes/pipelines.mdx
+2
-2
docs/source/quicktour.mdx
docs/source/quicktour.mdx
+2
-2
src/transformers/pipelines/base.py
src/transformers/pipelines/base.py
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docs/source/main_classes/pipelines.mdx
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cdf19c50
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@@ -78,7 +78,7 @@ GPU. If it doesn't don't hesitate to create an issue.
...
@@ -78,7 +78,7 @@ GPU. If it doesn't don't hesitate to create an issue.
```python
```python
import datasets
import datasets
from transformers import pipeline
from transformers import pipeline
from transformers.pipelines.
base
import KeyDataset
from transformers.pipelines.
pt_utils
import KeyDataset
from tqdm.auto import tqdm
from tqdm.auto import tqdm
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
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@@ -128,7 +128,7 @@ whenever the pipeline uses its streaming ability (so when passing lists or `Data
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@@ -128,7 +128,7 @@ whenever the pipeline uses its streaming ability (so when passing lists or `Data
```python
```python
from transformers import pipeline
from transformers import pipeline
from transformers.pipelines.
base
import KeyDataset
from transformers.pipelines.
pt_utils
import KeyDataset
import datasets
import datasets
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
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docs/source/quicktour.mdx
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cdf19c50
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@@ -118,7 +118,7 @@ Next, load a dataset (see the 🤗 Datasets [Quick Start](https://huggingface.co
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@@ -118,7 +118,7 @@ Next, load a dataset (see the 🤗 Datasets [Quick Start](https://huggingface.co
Now you can iterate over the dataset with the pipeline. `KeyDataset` retrieves the item in the dictionary returned by the dataset:
Now you can iterate over the dataset with the pipeline. `KeyDataset` retrieves the item in the dictionary returned by the dataset:
```py
```py
>>> from transformers.pipelines.
base
import KeyDataset
>>> from transformers.pipelines.
pt_utils
import KeyDataset
>>> from tqdm.auto import tqdm
>>> from tqdm.auto import tqdm
>>> for out in tqdm(speech_recognizer(KeyDataset(dataset, "file"))):
>>> for out in tqdm(speech_recognizer(KeyDataset(dataset, "file"))):
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src/transformers/pipelines/base.py
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cdf19c50
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@@ -49,6 +49,9 @@ if is_torch_available():
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@@ -49,6 +49,9 @@ if is_torch_available():
from
torch.utils.data
import
DataLoader
,
Dataset
from
torch.utils.data
import
DataLoader
,
Dataset
from
..models.auto.modeling_auto
import
AutoModel
from
..models.auto.modeling_auto
import
AutoModel
# Re-export for backward compatibility
from
.pt_utils
import
KeyDataset
else
:
else
:
Dataset
=
None
Dataset
=
None
KeyDataset
=
None
KeyDataset
=
None
...
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