deeppavlov/models/embedders/abstract_embedder.py
def _encode(self, tokens: List[str], mean: bool) -> Union[List[np.ndarray], np.ndarray]:
        """
        Embed one text sample

        Args:
            tokens: tokenized text sample
            mean: whether to return mean embedding of tokens per sample

        Returns:
            list of embedded tokens or array of mean values
        """
        embedded_tokens = []
        for t in tokens:
            try:
                emb = self.tok2emb[t]
            except KeyError:
                try:
                    emb = self._get_word_vector(t)
                except KeyError:
                    emb = np.zeros(self.dim, dtype=np.float32)
                self.tok2emb[t] = emb
            embedded_tokens.append(emb)

        if mean is None:
            mean = self.mean

        if mean:
            filtered = [et for et in embedded_tokens if np.any(et)]
            if filtered:
                return np.mean(filtered, axis=0)
            return np.zeros(self.dim, dtype=np.float32)

        return embedded_tokens
def _encode(self, tokens: List[str], mean: bool) -> Union[List[np.ndarray], np.ndarray]:
        """
        Embed one text sample

        Args:
            tokens: tokenized text sample
            mean: whether to return mean embedding of tokens per sample

        Returns:
            list of embedded tokens or array of mean values
        """
        embedded_tokens = []
        for t in tokens:
            try:
                emb = self.tok2emb[t]
            except KeyError:
                try:
                    emb = self._get_word_vector(t)
                except KeyError:
                    emb = np.zeros(self.dim, dtype=np.float32)
                self.tok2emb[t] = emb
            embedded_tokens.append(emb)

        if mean is None:
            mean = self.mean

        if mean:
            filtered = [et for et in embedded_tokens if np.any(et)]
            if filtered:
                return np.mean(filtered, axis=0)
            return np.zeros(self.dim, dtype=np.float32)

        return embedded_tokens
Embed one text sample

        Args:
            tokens: tokenized text sample
            mean: whether to return mean embedding of tokens per sample

        Returns:
            list of embedded tokens or array of mean values