Inferred Signatures for Hugging Face Pipelines¶

The Snowflake Model Registry automatically infers the signatures of Hugging Face pipelines containing a single task from the following list.

conversational
fill-mask
question-answering
summarization
table-question-answering
text2text-generation
text-classification (alias sentiment-analysis)
text-generation
token-classification (alias ner)
translation
translation_xx_to_yy
zero-shot-classification

This topic describes the signatures of these types of Hugging Face pipelines, including a description and example of the required inputs and expected outputs. All inputs and outputs are Snowpark DataFrames.

For general guidance about logging Hugging Face pipelines in the registry, see Hugging Face Pipeline.

Conversational Pipeline¶

A pipeline whose task is conversational has the following inputs and outputs.

Inputs¶

user_inputs: A list of strings representing the user’s previous and current inputs. The last one in the list is the current input.
generated_responses: A list of strings representing the model’s previous responses.

Example:

---------------------------------------------------------------------------
|"user_inputs"                                    |"generated_responses"  |
---------------------------------------------------------------------------
|[                                                |[                      |
|  "Do you speak French?",                        |  "Yes I do."          |
|  "Do you know how to say Snowflake in French?"  |]                      |
|]                                                |                       |
---------------------------------------------------------------------------

Outputs¶

generated_responses: A list of strings representing the model’s previous and current responses. The last one in the list is the current response.

Example:

-------------------------
|"generated_responses"  |
-------------------------
|[                      |
|  "Yes I do.",         |
|  "I speak French."    |
|]                      |
-------------------------

Copy

Fill Mask Pipeline¶

A pipeline who whose task is “fill-mask” has the following inputs and outputs.

Inputs¶

inputs: A string where there is a mask to fill.

Example:

--------------------------------------------------
|"inputs"                                        |
--------------------------------------------------
|LynYuu is the [MASK] of the Grand Duchy of Yu.  |
--------------------------------------------------

Outputs¶

outputs: A string containing a JSON representation of a list of reust objects, each of which may contain keys such as score, token, token_str, and sequence. See FillMaskPipeline for details.

Example:

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"outputs"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|[{"score": 0.9066258072853088, "token": 3007, "token_str": "capital", "sequence": "lynyuu is the capital of the grand duchy of yu."}, {"score": 0.08162177354097366, "token": 2835, "token_str": "seat", "sequence": "lynyuu is the seat of the grand duchy of yu."}, {"score": 0.0012052370002493262, "token": 4075, "token_str": "headquarters", "sequence": "lynyuu is the headquarters of the grand duchy of yu."}, {"score": 0.0006560495239682496, "token": 2171, "token_str": "name", "sequence": "lynyuu is the name of the grand duchy of yu."}, {"score": 0.0005427763098850846, "token": 3200, "token_str"...  |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Token Classification¶

A pipeline whose task is “ner” or “token-classification” has the following inputs and outputs.

Inputs¶

inputs: A string containing the tokens to be classified.

Example:

------------------------------------------------
|"inputs"                                      |
------------------------------------------------
|My name is Izumi and I live in Tokyo, Japan.  |
------------------------------------------------

Outputs¶

outputs: A string containing a JSON representation of a list of result objects, each of which may contain keys such as entity, score, index, word, name, start, and end. See TokenClassificationPipeline for details.

Example:

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"outputs"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|[{"entity": "PRON", "score": 0.9994392991065979, "index": 1, "word": "my", "start": 0, "end": 2}, {"entity": "NOUN", "score": 0.9968984127044678, "index": 2, "word": "name", "start": 3, "end": 7}, {"entity": "AUX", "score": 0.9937735199928284, "index": 3, "word": "is", "start": 8, "end": 10}, {"entity": "PROPN", "score": 0.9928083419799805, "index": 4, "word": "i", "start": 11, "end": 12}, {"entity": "PROPN", "score": 0.997334361076355, "index": 5, "word": "##zumi", "start": 12, "end": 16}, {"entity": "CCONJ", "score": 0.999173104763031, "index": 6, "word": "and", "start": 17, "end": 20}, {...  |

Question Answering (Single Output)¶

A pipeline whose task is “question-answering”, where top_k is either unset or set to 1, has the following inputs and outputs.

Inputs¶

question: A string containing the question to answer.
context: A string that may contain the answer.

Example:

-----------------------------------------------------------------------------------
|"question"                  |"context"                                           |
-----------------------------------------------------------------------------------
|What did Doris want to do?  |Doris is a cheerful mermaid from the ocean dept...  |
-----------------------------------------------------------------------------------

Outputs¶

score: Floating-point confidence score from 0.0 to 1.0.
start: Integer index of the first token of the answer in the context.
end: Integer index of the last token of the answer in the original context.
answer: A string containing the found answer.

Example:

--------------------------------------------------------------------------------
|"score"           |"start"  |"end"  |"answer"                                 |
--------------------------------------------------------------------------------
|0.61094731092453  |139      |178    |learn more about the world of athletics  |
--------------------------------------------------------------------------------

Question Answering (Multiple Outputs)¶

A pipeline whose task is “question-answering”, where top_k is set and is larger than 1, has the following inputs and outputs.

Inputs¶

question: A string containing the question to answer.
context: A string that may contain the answer.

Example:

-----------------------------------------------------------------------------------
|"question"                  |"context"                                           |
-----------------------------------------------------------------------------------
|What did Doris want to do?  |Doris is a cheerful mermaid from the ocean dept...  |
-----------------------------------------------------------------------------------

Outputs¶

outputs: A string containing a JSON representation of a list of result objects, each of which may contain keys such as score, start, end, and answer.

Example:

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"outputs"                                                                                                                                                                                                                                                                                                                                        |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|[{"score": 0.61094731092453, "start": 139, "end": 178, "answer": "learn more about the world of athletics"}, {"score": 0.17750297486782074, "start": 139, "end": 180, "answer": "learn more about the world of athletics.\""}, {"score": 0.06438097357749939, "start": 138, "end": 178, "answer": "\"learn more about the world of athletics"}]  |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Summarization¶

A pipeline whose task is “summarization”, where return_tensors is False or unset, has the following inputs and outputs.

Inputs¶

documents: A string containing text to summarize.

Example:

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"documents"                                                                                                                                                                                               |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|Neuro-sama is a chatbot styled after a female VTuber that hosts live streams on the Twitch channel "vedal987". Her speech and personality are generated by an artificial intelligence (AI) system  wh...  |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Outputs¶

summary_text: A string containing either the generated summary, or, if num_return_sequences is greater than 1, a string containing a JSON representation of a list of results, each a dictionary containing fields including summary_text.

Example:

---------------------------------------------------------------------------------
|"summary_text"                                                                 |
---------------------------------------------------------------------------------
| Neuro-sama is a chatbot styled after a female VTuber that hosts live streams  |
---------------------------------------------------------------------------------

Table Question Answering¶

A pipeline whose task is “table-question-answering” has the following inputs and outputs.

Inputs¶

query: A string containing the question to be answered.
table: A string containing a JSON-serialized dictionary in the form {column -> [values]} representing the table that may contain an answer.

Example:

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"query"                                  |"table"                                                                                                                                                                                                                                                   |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|Which channel has the most subscribers?  |{"Channel": ["A.I.Channel", "Kaguya Luna", "Mirai Akari", "Siro"], "Subscribers": ["3,020,000", "872,000", "694,000", "660,000"], "Videos": ["1,200", "113", "639", "1,300"], "Created At": ["Jun 30 2016", "Dec 4 2017", "Feb 28 2014", "Jun 23 2017"]}  |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Outputs¶

answer: A string containing a possible answer.
coordinates: A list of list of integers representing the coordinates of the cells where the answer was located.
cells: A list of strings containing the content of the cells where the answer was located.
aggregator: A string containing the name of the aggregator used.

Example:

----------------------------------------------------------------
|"answer"     |"coordinates"  |"cells"          |"aggregator"  |
----------------------------------------------------------------
|A.I.Channel  |[              |[                |NONE          |
|             |  [            |  "A.I.Channel"  |              |
|             |    0,         |]                |              |
|             |    0          |                 |              |
|             |  ]            |                 |              |
|             |]              |                 |              |
----------------------------------------------------------------

Text Classification (Single Output)¶

A pipeline whose task is “text-clasification” or “sentiment-analysis”, where top_k is not set or is None, has the following inputs and outputs.

Inputs¶

text: A string to classify.
text_pair: A string to classify along with text, used with models that compute text similarity. Leave empty if the model does not use it.

Example:

----------------------------------
|"text"       |"text_pair"       |
----------------------------------
|I like you.  |I love you, too.  |
----------------------------------

Outputs¶

label: A string representing the classification label of the text.
score: A floating-point confidence score from 0.0 to 1.0.

Example:

--------------------------------
|"label"  |"score"             |
--------------------------------
|LABEL_0  |0.9760091304779053  |
--------------------------------

Text Classification (Multiple Output)¶

A pipeline whose task is “text-clasification” or “sentiment-analysis”, where top_k is set to a number, has the following inputs and outputs.

Note

A text classification task is considered multiple-output if top_k is set to any number, even if that number is 1. To get a single output, use a top_k value of None.

Inputs¶

text: A string to classify.
text_pair: A string to classify along with text, used with models that compute text similarity. Leave empty if the model does not use it.

Example:

--------------------------------------------------------------------
|"text"                                              |"text_pair"  |
--------------------------------------------------------------------
|I am wondering if I should have udon or rice fo...  |             |
--------------------------------------------------------------------

Outputs¶

outputs: A string containing a JSON representation of a list of results, each of which contains fields including label and score.

Example:

--------------------------------------------------------
|"outputs"                                             |
--------------------------------------------------------
|[{"label": "NEGATIVE", "score": 0.9987024068832397}]  |
--------------------------------------------------------

Text Generation¶

A pipeline whose task is “text-generation”, where return_tensors is False or unset, has the following inputs and outputs.

Note

Text generation pipelines where return_tensors is True are not supported.

Inputs¶

inputs: A string containing a prompt.

Example:

--------------------------------------------------------------------------------
|"inputs"                                                                      |
--------------------------------------------------------------------------------
|A descendant of the Lost City of Atlantis, who swam to Earth while saying, "  |
--------------------------------------------------------------------------------

Outputs¶

outputs: A string containin a JSON representatino of a list of result objects, each of which contains fields including generated_text.

Example:

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"outputs"                                                                                                                                                                                                 |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|[{"generated_text": "A descendant of the Lost City of Atlantis, who swam to Earth while saying, \"For my life, I don't know if I'm gonna land upon Earth.\"\n\nIn \"The Misfits\", in a flashback, wh...  |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Text-to-text Generation¶

A pipeline whose task is “text2text-generation”, where return_tensors is False or unset, has the following inputs and outputs.

Note

Text-to-text generation pipelines where return_tensors is True are not supported.

Inputs¶

inputs: A string containing a prompt.

Example:

--------------------------------------------------------------------------------
|"inputs"                                                                      |
--------------------------------------------------------------------------------
|A descendant of the Lost City of Atlantis, who swam to Earth while saying, "  |
--------------------------------------------------------------------------------

Outputs¶

generated_text : A string containing the generated text if num_return_sequences is 1, or a string representation of a JSON list of result dictionaries containing fields including generated_text if num_return_sequences is greater than 1.

Example:

----------------------------------------------------------------
|"generated_text"                                              |
----------------------------------------------------------------
|, said that he was a descendant of the Lost City of Atlantis  |
----------------------------------------------------------------

Translation Generation¶

A pipeline whose task is “translation”, where return_tensors is False or unset, has the following inputs and outputs.

Note

Translation generation pipelines where return_tensors is True are not supported.

Inputs¶

inputs: A string containing text to translate.

Example:

------------------------------------------------------------------------------------------------------
|"inputs"                                                                                            |
------------------------------------------------------------------------------------------------------
|Snowflake's Data Cloud is powered by an advanced data platform provided as a self-managed service.  |
------------------------------------------------------------------------------------------------------

Outputs¶

translation_text: A string representing generated translation if num_return_sequences is 1, or a string representation of a JSON list of result dictionaries, each containing fields including translation_text.

Example:

---------------------------------------------------------------------------------------------------------------------------------
|"translation_text"                                                                                                             |
---------------------------------------------------------------------------------------------------------------------------------
|Le Cloud de données de Snowflake est alimenté par une plate-forme de données avancée fournie sous forme de service autogérés.  |
---------------------------------------------------------------------------------------------------------------------------------

Zero-shot Classification¶

A pipeline whose task is “zero-shot-classification” has the following inputs and outputs.

Inputs¶

sequences: A string containing the text to be classified
candidate_labels: A list of strings containing the labels to be applied to the text.

Example:

-----------------------------------------------------------------------------------------
|"sequences"                                                       |"candidate_labels"  |
-----------------------------------------------------------------------------------------
|I have a problem with Snowflake that needs to be resolved asap!!  |[                   |
|                                                                  |  "urgent",         |
|                                                                  |  "not urgent"      |
|                                                                  |]                   |
|I have a problem with Snowflake that needs to be resolved asap!!  |[                   |
|                                                                  |  "English",        |
|                                                                  |  "Japanese"        |
|                                                                  |]                   |
-----------------------------------------------------------------------------------------

Outputs¶

sequence: The input string.
labels: A list of strings representing the labels that were applied.
scores: A list of floating-point confidence scores for each label.

Example:

--------------------------------------------------------------------------------------------------------------
|"sequence"                                                        |"labels"        |"scores"                |
--------------------------------------------------------------------------------------------------------------
|I have a problem with Snowflake that needs to be resolved asap!!  |[               |[                       |
|                                                                  |  "urgent",     |  0.9952737092971802,   |
|                                                                  |  "not urgent"  |  0.004726255778223276  |
|                                                                  |]               |]                       |
|I have a problem with Snowflake that needs to be resolved asap!!  |[               |[                       |
|                                                                  |  "Japanese",   |  0.5790848135948181,   |
|                                                                  |  "English"     |  0.42091524600982666   |
|                                                                  |]               |]                       |
--------------------------------------------------------------------------------------------------------------