Model: DornierDo17/all-MiniLM-L6-v2-CosQA
Author: DornierDo17
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:9020, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:9020, loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget: source_sentence: python test path exists
sentences: def _pip_exists(self):
        """Returns True if pip exists inside the virtual environment. Can be
        used as a naive way to verify that the environment is installed."""
        return os.path.isfile(os.path.join(self.path, 'bin', 'pip')), def parse(self, s):
        """
        Parses a date string formatted like ``YYYY-MM-DD``.
        """
        return datetime.datetime.strptime(s, self.date_format).date(), def logout(self):
        """
            Logout from the remote server.
        """
        self.client.write('exit\r\n')
        self.client.read_all()
        self.client.close(), source_sentence: python remove words from sentences in a list
sentences: def _to_corrected_pandas_type(dt):
    """
    When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong.
    This method gets the corrected data type for Pandas if that type may be inferred uncorrectly.
    """
    import numpy as np
    if type(dt) == ByteType:
        return np.int8
    elif type(dt) == ShortType:
        return np.int16
    elif type(dt) == IntegerType:
        return np.int32
    elif type(dt) == FloatType:
        return np.float32
    else:
        return None, def filter_dict(d, keys):
    """
    Creates a new dict from an existing dict that only has the given keys
    """
    return {k: v for k, v in d.items() if k in keys}, def remove_empty_text(utterances: List[Utterance]) -> List[Utterance]:
    """Remove empty utterances from a list of utterances
    Args:
        utterances: The list of utterance we are processing
    """
    return [utter for utter in utterances if utter.text.strip() != ""], source_sentence: python numpy array fix dtype
sentences: def from_bytes(cls, b):
		"""Create :class:`PNG` from raw bytes.
		
		:arg bytes b: The raw bytes of the PNG file.
		:rtype: :class:`PNG`
		"""
		im = cls()
		im.chunks = list(parse_chunks(b))
		im.init()
		return im, def dict_to_numpy_array(d):
    """
    Convert a dict of 1d array to a numpy recarray
    """
    return fromarrays(d.values(), np.dtype([(str(k), v.dtype) for k, v in d.items()])), def _remove_blank(l):
        """ Removes trailing zeros in the list of integers and returns a new list of integers"""
        ret = []
        for i, _ in enumerate(l):
            if l[i] == 0:
                break
            ret.append(l[i])
        return ret, source_sentence: how to get the maximum cell in one row in python
sentences: def _run_sync(self, method: Callable, *args, **kwargs) -> Any:
        """
        Utility method to run commands synchronously for testing.
        """
        if self.loop.is_running():
            raise RuntimeError("Event loop is already running.")

        if not self.is_connected:
            self.loop.run_until_complete(self.connect())

        task = asyncio.Task(method(*args, **kwargs), loop=self.loop)
        result = self.loop.run_until_complete(task)

        self.loop.run_until_complete(self.quit())

        return result, def argmax(self, rows: List[Row], column: ComparableColumn) -> List[Row]:
        """
        Takes a list of rows and a column name and returns a list containing a single row (dict from
        columns to cells) that has the maximum numerical value in the given column. We return a list
        instead of a single dict to be consistent with the return type of ``select`` and
        ``all_rows``.
        """
        if not rows:
            return []
        value_row_pairs = [(row.values[column.name], row) for row in rows]
        if not value_row_pairs:
            return []
        # Returns a list containing the row with the max cell value.
        return [sorted(value_row_pairs, key=lambda x: x[0], reverse=True)[0][1]], def load_yaml(yaml_file: str) -> Any:
    """
    Load YAML from file.

    :param yaml_file: path to YAML file
    :return: content of the YAML as dict/list
    """
    with open(yaml_file, 'r') as file:
        return ruamel.yaml.load(file, ruamel.yaml.RoundTripLoader), source_sentence: check if address is valid python
sentences: def is_valid_ipv6(ip_str):
    """
    Check the validity of an IPv6 address
    """
    try:
        socket.inet_pton(socket.AF_INET6, ip_str)
    except socket.error:
        return False
    return True, def longest_run_1d(arr):
    """Return the length of the longest consecutive run of identical values.

    Parameters
    ----------
    arr : bool array
      Input array

    Returns
    -------
    int
      Length of longest run.
    """
    v, rl = rle_1d(arr)[:2]
    return np.where(v, rl, 0).max(), def dict_to_enum_fn(d: Dict[str, Any], enum_class: Type[Enum]) -> Enum:
    """
    Converts an ``dict`` to a ``Enum``.
    """
    return enum_class[d['name']]
pipeline_tag: sentence-similarity
library_name: sentence-transformers
Likes: 0 | All-time downloads: 78
Created: 2025-10-02 11:12:01