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## How to Use
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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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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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- `stsb` - [stsb-roberta-large](https://huggingface.co/sentence-transformers/stsb-roberta-large)
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- `use` - [Universal Sentence Encoder](https://huggingface.co/sentence-transformers/use-cmlm-multilingual) (Default)
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Refer to the inputs descriptions for more detailed usage as follows:
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### Output Values
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List of `Scores` dataclass corresponding to each sample -
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- `precision: float`: Precision score, which ranges from 0.0 to 1.0.
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- `recall: List[float]`: Recall score corresponding to each reference
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- `f1: float`: F1 score (between precision and average recall).
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## How to Use
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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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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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- `stsb` - [stsb-roberta-large](https://huggingface.co/sentence-transformers/stsb-roberta-large)
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- `use` - [Universal Sentence Encoder](https://huggingface.co/sentence-transformers/use-cmlm-multilingual) (Default)
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Furthermore, you can use any model on Huggingface/SentenceTransformer that is supported by SentenceTransformer
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such as `all-mpnet-base-v2` or `roberta-base`
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- `tokenize_sentences (bool)`: Flag to indicate whether to tokenize the sentences in the input documents. Default: True.
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- `multi_references (bool)`: Flag to indicate whether multiple references are provided. Default: False.
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- `gpu (Union[bool, str, int, List[Union[str, int]]])`: Whether to use GPU, CPU or multiple-processes for computation.
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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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### Output Values
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List of `Scores` dataclass corresponding to each sample -
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- `precision: float`: Precision score, which ranges from 0.0 to 1.0.
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- `recall: List[float]`: Recall score corresponding to each reference
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- `f1: float`: F1 score (between precision and average recall).
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