Model: Tombiczek/all-MiniLM-L6-v2_fine-tuned-cosqa
Author: Tombiczek
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:9008, 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, model-index, 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:9008, loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget: source_sentence: python get basename url
sentences: def get_url_file_name(url):
    """Get the file name from an url
    
    Parameters
    ----------
    url : str

    Returns
    -------
    str
        The file name 
    """

    assert isinstance(url, (str, _oldstr))
    return urlparse.urlparse(url).path.split('/')[-1], def exists(self):
        """
        Returns true if the job is still running or zero-os still knows about this job ID

        After a job is finished, a job remains on zero-os for max of 5min where you still can read the job result
        after the 5 min is gone, the job result is no more fetchable
        :return: bool
        """
        r = self._client._redis
        flag = '{}:flag'.format(self._queue)
        return bool(r.exists(flag)), def print_failure_message(message):
    """Print a message indicating failure in red color to STDERR.

    :param message: the message to print
    :type message: :class:`str`
    """
    try:
        import colorama

        print(colorama.Fore.RED + message + colorama.Fore.RESET,
              file=sys.stderr)
    except ImportError:
        print(message, file=sys.stderr), source_sentence: python tzlocal not defined
sentences: def rstjinja(app, docname, source):
    """
    Render our pages as a jinja template for fancy templating goodness.
    """
    # Make sure we're outputting HTML
    if app.builder.format != 'html':
        return
    src = source[0]
    rendered = app.builder.templates.render_string(
        src, app.config.html_context
    )
    source[0] = rendered, def _trim(image):
    """Trim a PIL image and remove white space."""
    background = PIL.Image.new(image.mode, image.size, image.getpixel((0, 0)))
    diff = PIL.ImageChops.difference(image, background)
    diff = PIL.ImageChops.add(diff, diff, 2.0, -100)
    bbox = diff.getbbox()
    if bbox:
        image = image.crop(bbox)
    return image, def reload_localzone():
    """Reload the cached localzone. You need to call this if the timezone has changed."""
    global _cache_tz
    _cache_tz = pytz.timezone(get_localzone_name())
    utils.assert_tz_offset(_cache_tz)
    return _cache_tz, source_sentence: get text from a url python
sentences: def _check_for_duplicate_sequence_names(self, fasta_file_path):
        """Test if the given fasta file contains sequences with duplicate
        sequence names.

        Parameters
        ----------
        fasta_file_path: string
            path to file that is to be checked

        Returns
        -------
        The name of the first duplicate sequence found, else False.

        """
        found_sequence_names = set()
        for record in SeqIO.parse(fasta_file_path, 'fasta'):
            name = record.name
            if name in found_sequence_names:
                return name
            found_sequence_names.add(name)
        return False, def as_list(callable):
    """Convert a scalar validator in a list validator"""
    @wraps(callable)
    def wrapper(value_iter):
        return [callable(value) for value in value_iter]

    return wrapper, def url_read_text(url, verbose=True):
    r"""
    Directly reads text data from url
    """
    data = url_read(url, verbose)
    text = data.decode('utf8')
    return text, source_sentence: get python dicitonary keys as list
sentences: def strip_spaces(x):
    """
    Strips spaces
    :param x:
    :return:
    """
    x = x.replace(b' ', b'')
    x = x.replace(b'\t', b'')
    return x, def get_points(self):
        """Returns a ketama compatible list of (position, nodename) tuples.
        """
        return [(k, self.runtime._ring[k]) for k in self.runtime._keys], def normalized_distance(self, image):
        """Calculates the distance of a given image to the
        original image.

        Parameters
        ----------
        image : `numpy.ndarray`
            The image that should be compared to the original image.

        Returns
        -------
        :class:`Distance`
            The distance between the given image and the original image.

        """
        return self.__distance(
            self.__original_image_for_distance,
            image,
            bounds=self.bounds()), source_sentence: python qpushbutton resize in grid layout
sentences: def trap_exceptions(results, handler, exceptions=Exception):
	"""
	Iterate through the results, but if an exception occurs, stop
	processing the results and instead replace
	the results with the output from the exception handler.
	"""
	try:
		for result in results:
			yield result
	except exceptions as exc:
		for result in always_iterable(handler(exc)):
			yield result, def __delitem__(self, resource):
        """Remove resource instance from internal cache"""
        self.__caches[type(resource)].pop(resource.get_cache_internal_key(), None), def resize(self, width, height):
        """
        Pyqt specific resize callback.
        """
        if not self.fbo:
            return

        # pyqt reports sizes in actual buffer size
        self.width = width // self.widget.devicePixelRatio()
        self.height = height // self.widget.devicePixelRatio()
        self.buffer_width = width
        self.buffer_height = height

        super().resize(width, height)
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@10, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: cosqa valid
type: cosqa-valid
metrics: type: cosine_accuracy@1
value: 0.802
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.964
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.992
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.998
name: Cosine Accuracy@10
verified: no, type: cosine_precision@10
value: 0.09980000000000001
name: Cosine Precision@10
verified: no, type: cosine_recall@10
value: 0.998
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9137103626158444
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8851079365079363
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8851579365079365
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 25
Created: 2025-10-30 12:01:09