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README.md
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@@ -44,7 +44,7 @@ This metric takes as input lists of predicted sentences and reference sentences:
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### Inputs
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- **predictions** (`list` of `str`s): List of generated sentences to score.
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- **references** (`list` of `str`s): List of references to compare to.
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- **
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### Output Values
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- **scores** : a `list` of scores, one per prediction.
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@@ -79,7 +79,7 @@ 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", 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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### Inputs
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- **predictions** (`list` of `str`s): List of generated sentences to score.
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- **references** (`list` of `str`s): List of references to compare to.
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- **config_name** (`str`): BLEURT checkpoint. Will default to `BLEURT-tiny` if not specified. Other models that can be chosen are: `"bleurt-tiny-128"`, `"bleurt-tiny-512"`, `"bleurt-base-128"`, `"bleurt-base-512"`, `"bleurt-large-128"`, `"bleurt-large-512"`, `"BLEURT-20-D3"`, `"BLEURT-20-D6"`, `"BLEURT-20-D12"` and `"BLEURT-20"`.
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### Output Values
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- **scores** : a `list` of scores, one per prediction.
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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", config\_name="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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