Hugging Face 管道的推断签名¶
Snowflake Model Registry 会自动推断包含以下列表中的单个任务的 Hugging Face 管道的签名：
conversationalfill-maskquestion-answeringsummarizationtable-question-answeringtext2text-generationtext-classification（别名：sentiment-analysis）text-generationtoken-classification（别名：ner）translationtranslation_xx_to_yyzero-shot-classification
本主题介绍以下这些类型的 Hugging Face (link removed) 管道的签名，包括所需输入和预期输出的描述和示例。所有输入和输出均为 Snowpark DataFrames。
有关在注册表中记录 Hugging Face 管道的一般指南，请参阅 Hugging Face pipeline。
对话管道¶
` 对话 <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.ConversationalPipeline (link removed)>`_ 任务的管道具有以下输入和输出。
输入¶
user_inputs：表示用户先前和当前输入的字符串列表。列表中的最后一个是当前输入。generated_responses：表示模型先前响应的字符串列表。
示例：
---------------------------------------------------------------------------
|"user_inputs" |"generated_responses" |
---------------------------------------------------------------------------
|[ |[ |
| "Do you speak French?", | "Yes I do." |
| "Do you know how to say Snowflake in French?" |] |
|] | |
---------------------------------------------------------------------------
输出¶
generated_responses：表示模型先前和当前响应的字符串列表。列表中的最后一个是当前响应。
示例：
-------------------------
|"generated_responses" |
-------------------------
|[ |
| "Yes I do.", |
| "I speak French." |
|] |
-------------------------
Fill-mask 管道¶
“Fill-mask (link removed)”任务的管道具有以下输入和输出。
输入¶
inputs：要填充掩码的字符串。
示例：
--------------------------------------------------
|"inputs" |
--------------------------------------------------
|LynYuu is the [MASK] of the Grand Duchy of Yu. |
--------------------------------------------------
输出¶
outputs：一个字符串，包含以 JSON 格式表示的对象列表，列表中的每个对象都可能包含score、token、token_str或sequence等键。 有关详细信息，请参阅 FillMaskPipeline (link removed)。
示例：
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"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"... |
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令牌分类¶
“ner”或“` token-classification <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TokenClassificationPipeline (link removed)>`_”任务的管道具有以下输入和输出。
输入¶
inputs：包含要分类的令牌的字符串。
示例：
------------------------------------------------
|"inputs" |
------------------------------------------------
|My name is Izumi and I live in Tokyo, Japan. |
------------------------------------------------
输出¶
outputs：一个字符串，包含以 JSON 格式表示的结果对象的列表，列表中的每个对象都可能包含entity、score、index、word、name、start或end等键。 有关详细信息，请参阅 TokenClassificationPipeline <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TokenClassificationPipeline (link removed)>`_。
示例：
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"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}, {... |
问答（单个输出）¶
“` question-answering <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.QuestionAnsweringPipeline (link removed)>`_”任务的管道，其中 top_k 未设置或设置为 1，具有以下输入和输出。
输入¶
question：包含要回答的问题的字符串。context：可能包含答案的字符串。
示例：
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|"question" |"context" |
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|What did Doris want to do? |Doris is a cheerful mermaid from the ocean dept... |
-----------------------------------------------------------------------------------
输出¶
score：浮点置信度分数从 0.0 到 1.0。start：在上下文中，答案第一个词元的整数索引。end：在原始上下文中，答案最后一个词元的整数索引。answer：包含找到的答案的字符串。
示例：
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|"score" |"start" |"end" |"answer" |
--------------------------------------------------------------------------------
|0.61094731092453 |139 |178 |learn more about the world of athletics |
--------------------------------------------------------------------------------
问答（多个输出）¶
任务是“` question-answering <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.QuestionAnsweringPipeline (link removed)>`_”的管道，其中 top_k 设置为大于 1，具有以下输入和输出。
输入¶
question：包含要回答的问题的字符串。context：可能包含答案的字符串。
示例：
-----------------------------------------------------------------------------------
|"question" |"context" |
-----------------------------------------------------------------------------------
|What did Doris want to do? |Doris is a cheerful mermaid from the ocean dept... |
-----------------------------------------------------------------------------------
输出¶
outputs：一个字符串，包含以 JSON 格式表示的结果对象的列表，列表中的每个对象都可能包含score、start、end或answer等键。
示例：
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"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"}] |
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摘要¶
任务是“` summarization <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.SummarizationPipeline (link removed)>`_”的管道，其中 return_tensors 为 False 或未设置，具有以下输入和输出。
输入¶
documents：包含要汇总的文本的字符串。
示例：
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"documents" |
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|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... |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
输出¶
summary_text：包含生成的摘要的字符串，或者，如果num_return_sequences大于 1，则字符串包含以 JSON 格式表示的结果列表，每个结果都是一个包含字段的字典，其中包括summary_text。
示例：
---------------------------------------------------------------------------------
|"summary_text" |
---------------------------------------------------------------------------------
| Neuro-sama is a chatbot styled after a female VTuber that hosts live streams |
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表问答¶
任务是“` table-question-answering <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TableQuestionAnsweringPipeline (link removed)>`_”的管道具有以下输入和输出。
输入¶
query：包含要回答的问题的字符串。table：包含 JSON 序列化字典的字符串，形式为{column -> [values]}，表示可能包含答案的表。
示例：
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|"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"]} |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
输出¶
answer：包含可能答案的字符串。coordinates：表示答案所在的单元格坐标的整数列表。cells：包含答案所在的单元格内容的字符串列表。aggregator：包含所用聚合器名称的字符串。
示例：
----------------------------------------------------------------
|"answer" |"coordinates" |"cells" |"aggregator" |
----------------------------------------------------------------
|A.I.Channel |[ |[ |NONE |
| | [ | "A.I.Channel" | |
| | 0, |] | |
| | 0 | | |
| | ] | | |
| |] | | |
----------------------------------------------------------------
文本分类（单个输出）¶
“text-clasification (link removed)”任务的管道，其中 top_k 未设置或为 None，具有以下输入和输出。
输入¶
text：要分类的字符串。text_pair：与text一起分类的字符串，用于计算文本相似度的模型。如果模型不使用它，则留空。
示例：
----------------------------------
|"text" |"text_pair" |
----------------------------------
|I like you. |I love you, too. |
----------------------------------
输出¶
label：表示文本分类标签的字符串。score：浮点置信度分数从 0.0 到 1.0。
示例：
--------------------------------
|"label" |"score" |
--------------------------------
|LABEL_0 |0.9760091304779053 |
--------------------------------
文本分类（多个输出）¶
“text-clasification (link removed)”任务的管道，其中 top_k 设置为一个数字，具有以下输入和输出。
备注
如果将 top_k 设置为任何数字，即使该数字为 1，文本分类任务也被视为多个输出。要获取 单个输出，请将 top_k 值设为 None。
输入¶
text：要分类的字符串。text_pair：与text一起分类的字符串，用于计算文本相似度的模型。如果模型不使用它，则留空。
示例：
--------------------------------------------------------------------
|"text" |"text_pair" |
--------------------------------------------------------------------
|I am wondering if I should have udon or rice fo... | |
--------------------------------------------------------------------
输出¶
outputs：一个字符串，包含以 JSON 格式表示的结果列表，每个结果都包含包括label和score的字段。
示例：
--------------------------------------------------------
|"outputs" |
--------------------------------------------------------
|[{"label": "NEGATIVE", "score": 0.9987024068832397}] |
--------------------------------------------------------
文本生成¶
任务是“` text-generation <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TextGenerationPipeline (link removed)>`_”的管道，其中 return_tensors 为 False 或未设置，具有以下输入和输出。
备注
文本生成管道，其中 return_tensors 是 True，不受支持。
输入¶
inputs：包含提示的字符串。
示例：
--------------------------------------------------------------------------------
|"inputs" |
--------------------------------------------------------------------------------
|A descendant of the Lost City of Atlantis, who swam to Earth while saying, " |
--------------------------------------------------------------------------------
输出¶
outputs：一个字符串，包含以 JSON 格式表示的结果对象的列表，每个对象都包含包括generated_text的字段。
示例：
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"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... |
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文本到文本的生成¶
任务是“` text2text-generation <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.Text2TextGenerationPipeline (link removed)>`_”的管道，其中 return_tensors 为 False 或未设置，具有以下输入和输出。
备注
文本到文本的生成管道，其中 return_tensors 是 True，不受支持。
输入¶
inputs：包含提示的字符串。
示例：
--------------------------------------------------------------------------------
|"inputs" |
--------------------------------------------------------------------------------
|A descendant of the Lost City of Atlantis, who swam to Earth while saying, " |
--------------------------------------------------------------------------------
输出¶
generated_text：如果
num_return_sequences为 1，则为包含生成文本的字符串；如果 num_return_sequences 大于 1，则为以 JSON 格式表示字典结果列表的字符串，字典包含generated_text在内的字段。
示例：
----------------------------------------------------------------
|"generated_text" |
----------------------------------------------------------------
|, said that he was a descendant of the Lost City of Atlantis |
----------------------------------------------------------------
翻译生成¶
任务是“` translation <https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TranslationPipeline (link removed)>`_”的管道，其中 return_tensors 为 False 或未设置，具有以下输入和输出。
备注
翻译生成管道，其中 return_tensors 是 True，不受支持。
输入¶
inputs：包含要翻译的文本的字符串。
示例：
------------------------------------------------------------------------------------------------------
|"inputs" |
------------------------------------------------------------------------------------------------------
|Snowflake's Data Cloud is powered by an advanced data platform provided as a self-managed service. |
------------------------------------------------------------------------------------------------------
输出¶
translation_text：如果num_return_sequences为 1，则为表示生成的翻译的字符串，或者是以 JSON 格式表示字典结果列表的字符串，每个字典均包含包括translation_text的字段。
示例：
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|"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 分类¶
“zero-shot-classification (link removed)”任务的管道具有以下输入和输出。
输入¶
sequences：包含要分类的文本的字符串。candidate_labels：包含要应用于文本的标签的字符串列表。
示例：
-----------------------------------------------------------------------------------------
|"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" |
| |] |
-----------------------------------------------------------------------------------------
输出¶
sequence：输入字符串。labels：表示已应用的标签的字符串列表。scores：每个标签的浮点置信度分数列表。
示例：
--------------------------------------------------------------------------------------------------------------
|"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 |
| |] |] |
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