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1877 add normalize and truncate #1885

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52126f9
Adding normalize and truncate to `feature_extraction`
datavistics Nov 30, 2023
d1a9aaa
Adding _async_client.py changes
datavistics Dec 4, 2023
dedd15e
Updating mypy requirements
datavistics Dec 4, 2023
92d9b9d
Merge branch 'main' into 1877-normalize-truncate
datavistics Dec 5, 2023
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22 changes: 20 additions & 2 deletions src/huggingface_hub/inference/_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -481,7 +481,14 @@ def document_question_answering(
response = self.post(json=payload, model=model, task="document-question-answering")
return _bytes_to_list(response)

def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.ndarray":
def feature_extraction(
self,
text: str,
*,
model: Optional[str] = None,
truncate: Optional[bool] = None,
normalize: Optional[bool] = None,
) -> "np.ndarray":
"""
Generate embeddings for a given text.

Expand All @@ -492,6 +499,10 @@ def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.n
The model to use for the conversational 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 conversational model will be used.
Defaults to None.
truncate (`bool`, *optional*):
Whether to truncate in the tokenization step
normalize (`bool`, *optional*):
Whether to apply L2 normalization on the server after feature extraction

Returns:
`np.ndarray`: The embedding representing the input text as a float32 numpy array.
Expand All @@ -511,9 +522,16 @@ def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.n
[-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ],
...,
[ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32)
>>> client.feature_extraction("Hi, who are you?"*10_000, truncate=True)
array([[-3.41086648e-02, 2.50333734e-02, ..., -1.86223872e-02, 1.07370131e-03]], dtype=float32)
```
"""
response = self.post(json={"inputs": text}, model=model, task="feature-extraction")
payload: Dict[str, Any] = {"inputs": text}
if truncate is not None:
payload["truncate"] = truncate
if normalize is not None:
payload["normalize"] = normalize
response = self.post(json=payload, model=model, task="feature-extraction")
np = _import_numpy()
return np.array(_bytes_to_dict(response), dtype="float32")

Expand Down
22 changes: 20 additions & 2 deletions src/huggingface_hub/inference/_generated/_async_client.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -481,7 +481,14 @@ async def document_question_answering(
response = await self.post(json=payload, model=model, task="document-question-answering")
return _bytes_to_list(response)

async def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.ndarray":
async def feature_extraction(
self,
text: str,
*,
model: Optional[str] = None,
truncate: Optional[bool] = None,
normalize: Optional[bool] = None,
) -> "np.ndarray":
"""
Generate embeddings for a given text.

Expand All @@ -492,6 +499,10 @@ async def feature_extraction(self, text: str, *, model: Optional[str] = None) ->
The model to use for the conversational 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 conversational model will be used.
Defaults to None.
truncate (`bool`, *optional*):
Whether to truncate in the tokenization step
normalize (`bool`, *optional*):
Whether to apply L2 normalization on the server after feature extraction

Returns:
`np.ndarray`: The embedding representing the input text as a float32 numpy array.
Expand All @@ -512,9 +523,16 @@ async def feature_extraction(self, text: str, *, model: Optional[str] = None) ->
[-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ],
...,
[ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32)
>>> await client.feature_extraction("Hi, who are you?"*10_000, truncate=True)
array([[-3.41086648e-02, 2.50333734e-02, ..., -1.86223872e-02, 1.07370131e-03]], dtype=float32)
```
"""
response = await self.post(json={"inputs": text}, model=model, task="feature-extraction")
payload: Dict[str, Any] = {"inputs": text}
if truncate is not None:
payload["truncate"] = truncate
if normalize is not None:
payload["normalize"] = normalize
response = await self.post(json=payload, model=model, task="feature-extraction")
np = _import_numpy()
return np.array(_bytes_to_dict(response), dtype="float32")

Expand Down