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README.md
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@@ -30,9 +30,9 @@ Sem-F1 takes 2 mandatory arguments:
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- `predictions` - List of predictions. Format varies based on `tokenize_sentences` and `multi_references` flags.
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- `references`: List of references. Format varies based on `tokenize_sentences` and `multi_references` flags.
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```python
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from evaluate import load
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-
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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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@@ -47,6 +47,7 @@ for score in results:
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print(f"Precision: {score.precision}, Recall: {score.recall}, F1: {score.f1}")
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```
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Sem-F1 also accepts multiple optional arguments:
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- `model_type (str)`: Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
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- `pv1` - [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1)
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- `predictions` - List of predictions. Format varies based on `tokenize_sentences` and `multi_references` flags.
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- `references`: List of references. Format varies based on `tokenize_sentences` and `multi_references` flags.
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```python
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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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print(f"Precision: {score.precision}, Recall: {score.recall}, F1: {score.f1}")
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```
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+
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Sem-F1 also accepts multiple optional arguments:
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- `model_type (str)`: Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
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- `pv1` - [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1)
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