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1.1 KiB
Text Embedding with data2vec
author: David Wang
Description
This operator extracts features for text with data2vec. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.
Code Example
Use the pre-trained model to generate a text embedding for the sentence "Hello, world.".
Write the pipeline in simplified style:
import towhee
towhee.dc(["Hello, world."]) \
.text_embedding.data2vec() \
.show()
Factory Constructor
Create the operator via the following factory method
data2vec(model_name='facebook/data2vec-text-base')
Parameters:
model_name: str
The model name in string. The default value is "facebook/data2vec-text-base".
Supported model name:
- facebook/data2vec-text-base
Interface
Parameters:
text: str
The text in string.
Returns: numpy.ndarray
The text embedding extracted by model.
1.1 KiB
Text Embedding with data2vec
author: David Wang
Description
This operator extracts features for text with data2vec. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.
Code Example
Use the pre-trained model to generate a text embedding for the sentence "Hello, world.".
Write the pipeline in simplified style:
import towhee
towhee.dc(["Hello, world."]) \
.text_embedding.data2vec() \
.show()
Factory Constructor
Create the operator via the following factory method
data2vec(model_name='facebook/data2vec-text-base')
Parameters:
model_name: str
The model name in string. The default value is "facebook/data2vec-text-base".
Supported model name:
- facebook/data2vec-text-base
Interface
Parameters:
text: str
The text in string.
Returns: numpy.ndarray
The text embedding extracted by model.