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Fix optional model in tabular tasks #2018

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merged 3 commits into from
Feb 7, 2024
Merged

Fix optional model in tabular tasks #2018

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3c5b397
Fix optional model in tabular tasks
Wauplin Feb 6, 2024
78a2b75
Apply suggestions from code review
Wauplin Feb 7, 2024
766829b
make style
Wauplin Feb 7, 2024
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Fix optional model in tabular tasks
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@Wauplin
Wauplin committed Feb 6, 2024
commit 3c5b39728a3e256ab6166b57c5d3dfdcd41ba7d1
18 changes: 10 additions & 8 deletions src/huggingface_hub/inference/_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1063,16 +1063,17 @@ def table_question_answering(
)
return _bytes_to_dict(response) # type: ignore

def tabular_classification(self, table: Dict[str, Any], *, model: str) -> List[str]:
def tabular_classification(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[str]:
"""
Classifying a target category (a group) based on a set of attributes.

Args:
table (`Dict[str, Any]`):
Set of attributes to classify.
model (`str`):
The model to use for the tabular-classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.
model (`str`, *optional*):
The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.

Returns:
`List`: a list of labels, one per row in the initial table.
Expand Down Expand Up @@ -1107,16 +1108,17 @@ def tabular_classification(self, table: Dict[str, Any], *, model: str) -> List[s
response = self.post(json={"table": table}, model=model, task="tabular-classification")
return _bytes_to_list(response)

def tabular_regression(self, table: Dict[str, Any], *, model: str) -> List[float]:
def tabular_regression(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[float]:
"""
Predicting a numerical target value given a set of attributes/features in a table.

Args:
table (`Dict[str, Any]`):
Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.
model (`str`):
The model to use for the tabular-regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.
model (`str`, *optional*):
The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.

Returns:
`List`: a list of predicted numerical target values.
Expand Down
18 changes: 10 additions & 8 deletions src/huggingface_hub/inference/_generated/_async_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1080,16 +1080,17 @@ async def table_question_answering(
)
return _bytes_to_dict(response) # type: ignore

async def tabular_classification(self, table: Dict[str, Any], *, model: str) -> List[str]:
async def tabular_classification(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[str]:
"""
Classifying a target category (a group) based on a set of attributes.

Args:
table (`Dict[str, Any]`):
Set of attributes to classify.
model (`str`):
The model to use for the tabular-classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.
model (`str`, *optional*):
The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.

Returns:
`List`: a list of labels, one per row in the initial table.
Expand Down Expand Up @@ -1125,16 +1126,17 @@ async def tabular_classification(self, table: Dict[str, Any], *, model: str) ->
response = await self.post(json={"table": table}, model=model, task="tabular-classification")
return _bytes_to_list(response)

async def tabular_regression(self, table: Dict[str, Any], *, model: str) -> List[float]:
async def tabular_regression(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[float]:
"""
Predicting a numerical target value given a set of attributes/features in a table.

Args:
table (`Dict[str, Any]`):
Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.
model (`str`):
The model to use for the tabular-regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.
model (`str`, *optional*):
The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.

Returns:
`List`: a list of predicted numerical target values.
Expand Down
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