class
EmbeddingResult
[source]
EmbeddingResult
(sentences
:List
[Sentence
]) ::SentenceResult
A result class designed for Embedding models
Parameters:
sentences
:typing.List[flair.data.Sentence]
A list of Flair `Sentence`s
EmbeddingResult.sentence_embeddings
[source]
All embeddings in sentences
(if available)
EmbeddingResult.token_embeddings
[source]
All embeddings from the individual tokens in sentence
with original order in shape (n, embed_dim)
EmbeddingResult.to_dict
[source]
EmbeddingResult.to_dict
(detail_level
:DetailLevel
='low'
)
Returns self
as a dictionary
Parameters:
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return
class
EasyWordEmbeddings
[source]
EasyWordEmbeddings
()
Word embeddings from the latest language models
Usage:
>>> embeddings = adaptnlp.EasyWordEmbeddings()
>>> embeddings.embed_text("text you want embeddings for", model_name_or_path="bert-base-cased")
EasyWordEmbeddings.embed_text
[source]
EasyWordEmbeddings.embed_text
(text
:Union
[List
[Sentence
],Sentence
,List
[str
],str
],model_name_or_path
:Union
[str
,HFModelResult
,FlairModelResult
]='bert-base-cased'
,detail_level
:DetailLevel
='low'
,raw
:bool
=False
)
Produces embeddings for text
Parameters:
text
:typing.Union[typing.List[flair.data.Sentence], flair.data.Sentence, typing.List[str], str]
Text input, it can be a string or any of Flair's `Sentence` input formats
model_name_or_path
:typing.Union[str, adaptnlp.model_hub.HFModelResult, adaptnlp.model_hub.FlairModelResult]
, optionalThe hosted model name key, model path, or an instance of either `HFModelResult` or `FlairModelResult`
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return. By default is None, which returns a EmbeddingResult, otherwise will return a dict
raw
:<class 'bool'>
, optionalWhether to return the raw outputs
Returns:
typing.List[adaptnlp.inference.embeddings.EmbeddingResult]
A list of either `EmbeddingResult`s or dictionaries with information
EasyWordEmbeddings.embed_all
[source]
EasyWordEmbeddings.embed_all
(text
:Union
[List
[Sentence
],Sentence
,List
[str
],str
],model_names_or_paths
:List
[str
]=[]
,detail_level
:DetailLevel
='low'
)
Embeds text with all embedding models loaded
Parameters:
text
:typing.Union[typing.List[flair.data.Sentence], flair.data.Sentence, typing.List[str], str]
Text input, it can be a string or any of Flair's `Sentence` input formats
model_names_or_paths
:typing.List[str]
, optionalA list of model names
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return. By default is None, which returns a EmbeddingResult, otherwise will return a dict
Returns:
typing.List[adaptnlp.inference.embeddings.EmbeddingResult]
A list of either `EmbeddingResult`s or dictionaries with information
class
EasyStackedEmbeddings
[source]
EasyStackedEmbeddings
(*embeddings
:str
)
Word Embeddings that have been concatenated and 'stacked' as specified by Flair
Parameters:
embeddings
:<class 'str'>
EasyStackedEmbeddings.embed_text
[source]
EasyStackedEmbeddings.embed_text
(text
:Union
[List
[Sentence
],Sentence
,List
[str
],str
],detail_level
:DetailLevel
='low'
)
Stacked embeddings
Parameters:
text
:typing.Union[typing.List[flair.data.Sentence], flair.data.Sentence, typing.List[str], str]
Text input, it can be a string or any of Flair's `Sentence` input formats
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return. By default is None, which returns a EmbeddingResult, otherwise will return a dict
Returns:
typing.List[adaptnlp.inference.embeddings.EmbeddingResult]
A list of either EmbeddingResult's or dictionaries with information
class
EasyDocumentEmbeddings
[source]
EasyDocumentEmbeddings
(*embeddings
:str
,methods
:List
[str
]=['rnn', 'pool']
,configs
:Dict
[KT
,VT
]={'pool_configs': {'fine_tune_mode': 'linear', 'pooling': 'mean'}, 'rnn_configs': {'hidden_size': 512, 'rnn_layers': 1, 'reproject_words': True, 'reproject_words_dimension': 256, 'bidirectional': False, 'dropout': 0.5, 'word_dropout': 0.0, 'locked_dropout': 0.0, 'rnn_type': 'GRU', 'fine_tune': True}}
)
Document Embeddings generated by pool and rnn methods applied to the word embeddings of text
Parameters:
embeddings
:<class 'str'>
methods
:typing.List[str]
, optionalconfigs
:typing.Dict
, optional
EasyDocumentEmbeddings.embed_pool
[source]
EasyDocumentEmbeddings.embed_pool
(text
:Union
[List
[Sentence
],Sentence
,List
[str
],str
],detail_level
:DetailLevel
='low'
)
Generate stacked embeddings with DocumentPoolEmbeddings
Parameters:
text
:typing.Union[typing.List[flair.data.Sentence], flair.data.Sentence, typing.List[str], str]
Text input, it can be a string or any of Flair's `Sentence` input formats
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return. By default is None, which returns a EmbeddingResult, otherwise will return a dict
Returns:
typing.List[adaptnlp.inference.embeddings.EmbeddingResult]
A list of either EmbeddingResult's or dictionaries with information
EasyDocumentEmbeddings.embed_rnn
[source]
EasyDocumentEmbeddings.embed_rnn
(text
:Union
[List
[Sentence
],Sentence
,List
[str
],str
],detail_level
:DetailLevel
='low'
)
Generate stacked embeddings with DocumentRNNEmbeddings
Parameters:
text
:typing.Union[typing.List[flair.data.Sentence], flair.data.Sentence, typing.List[str], str]
Text input, it can be a string or any of Flair's `Sentence` input formats
detail_level
:<class 'fastcore.basics.DetailLevel'>
, optionalA level of detail to return. By default is None, which returns a EmbeddingResult, otherwise will return a dict
Returns:
typing.List[flair.data.Sentence]
A list of either EmbeddingResult's or dictionaries with information