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README.md
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@@ -34,14 +34,14 @@ Sem-F1 takes 2 mandatory arguments:
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from evaluate import load
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predictions = [
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["I go to School.
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["I love adventure sports."],
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]
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references = [
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["I go to School.
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["I love outdoor sports."],
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]
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metric = load("semf1")
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results = metric.compute(predictions=predictions, references=references)
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for score in results:
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print(f"Precision: {score.precision}, Recall: {score.recall}, F1: {score.f1}")
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@@ -58,11 +58,12 @@ Sem-F1 also accepts multiple optional arguments:
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- `batch_size (int)`: Batch size for encoding. Default: 32.
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- `verbose (bool)`: Flag to indicate verbose output. Default: False.
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Refer to the inputs descriptions for more detailed usage as follows
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```python
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import evaluate
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metric = evaluate.load("semf1")
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metric.inputs_description
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```
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from evaluate import load
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predictions = [
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["I go to School. You are stupid."],
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["I love adventure sports."],
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]
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references = [
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["I go to School. You are stupid."],
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["I love outdoor sports."],
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]
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metric = load("nbansal/semf1")
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results = metric.compute(predictions=predictions, references=references)
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for score in results:
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print(f"Precision: {score.precision}, Recall: {score.recall}, F1: {score.f1}")
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- `batch_size (int)`: Batch size for encoding. Default: 32.
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- `verbose (bool)`: Flag to indicate verbose output. Default: False.
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Refer to the inputs descriptions for more detailed usage as follows:
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```python
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import evaluate
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metric = evaluate.load("nbansal/semf1")
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print(metric.inputs_description)
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```
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