lvwerra HF staff commited on
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3980dde
1 Parent(s): 981697b

Update Space (evaluate main: 50512323)

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  1. README.md +3 -3
README.md CHANGED
@@ -31,7 +31,7 @@ This metric takes as input lists of predicted sentences and reference sentences:
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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- >>> bleurt = load("bleurt", type="metric")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  ```
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@@ -63,7 +63,7 @@ Example with the default model:
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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- >>> bleurt = load("bleurt", type="metric")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  >>> print(results)
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  {'scores': [1.0295498371124268, 1.0445425510406494]}
@@ -73,7 +73,7 @@ Example with the `"bleurt-base-128"` model checkpoint:
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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- >>> bleurt = load("bleurt", type="metric", checkpoint="bleurt-base-128")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  >>> print(results)
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  {'scores': [1.0295498371124268, 1.0445425510406494]}
 
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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+ >>> bleurt = load("bleurt", module_type="metric")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  ```
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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+ >>> bleurt = load("bleurt", module_type="metric")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  >>> print(results)
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  {'scores': [1.0295498371124268, 1.0445425510406494]}
 
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  ```python
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  >>> predictions = ["hello there", "general kenobi"]
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  >>> references = ["hello there", "general kenobi"]
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+ >>> bleurt = load("bleurt", module_type="metric", checkpoint="bleurt-base-128")
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  >>> results = bleurt.compute(predictions=predictions, references=references)
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  >>> print(results)
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  {'scores': [1.0295498371124268, 1.0445425510406494]}