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Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 2 years ago
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  1. 24
      README.md
  2. BIN
      result.png

24
README.md

@ -19,27 +19,19 @@ Use the pre-trained model "microsoft/unixcoder-base"
to generate text embeddings for given to generate text embeddings for given
text description "return max value" and code "def max(a,b): if a>b: return a else return b". text description "return max value" and code "def max(a,b): if a>b: return a else return b".
*Write the pipeline*:
*Write a pipeline with explicit inputs/outputs name specifications:*
```python ```python
import towhee
from towhee.dc2 import pipe, ops, DataCollection
(
towhee.dc(['find max value', 'def max(a,b): if a>b: return a else return b'])
.code_search.unixcoder(model_name='microsoft/unixcoder-base')
p = (
pipe.input('text')
.map('text', 'embedding', ops.code_search.unixcoder())
.output('text', 'embedding')
) )
```
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
(
towhee.dc['text'](['return max value', 'def max(a,b): if a>b: return a else return b'])
.code_search.unixcoder['text', 'embedding']()
.show()
)
DataCollection(p('find max value')).show()
DataCollection(p('def max(a,b): if a>b: return a else return b')).show()
``` ```
<img src="./result.png" width="800px"/> <img src="./result.png" width="800px"/>

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