Model: hojzas/setfit-proj8-all-mpnet-base-v2
Author: hojzas
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, dataset:hojzas/proj8-label2, arxiv:2209.11055, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, co2_eq_emissions, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: library_name: setfit
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
datasets: hojzas/proj8-label2
metrics: accuracy
widget: def first_with_given_key(iterable, key=lambda y: y):
    done=list()
    it=iter(iterable)
    while not False:
        try:
            value=next(it)
            if key(value) not in done:
                yield value
                done.insert(-1,key(value))
        except StopIteration:
            break, def first_with_given_key(iterable, key=lambda y: y):
    it=iter(iterable)
    li=list()
    while not False:
        try:
            hodnota=next(it)
            if key(hodnota) not in li:
                yield hodnota
                li.insert(-1,key(hodnota))
        except StopIteration:
            break, def first_with_given_key(iterable, key=lambda item : item):
    key_list = set()
    it = iter(iterable)
    while True:
        try:
            item = next(it)
            value = key(item)
            try:
                value = hash(value)
            except TypeError:
                value = repr(value)
            if value not in key_list:
                key_list.add(value)
                yield item
        except StopIteration:
            break, def first_with_given_key(iterable, key=lambda x: x):
    done=list()
    for value in iterable:
        if key(value) not in done:
            yield value
            done.insert(-1,key(value)), def first_with_given_key(iterable, key=repr):
    used_keys = {}
    get_key = return_key(key)
    for item in iterable:
        item_key = get_key(item)
        if item_key in used_keys.keys():
            continue
        try:
            used_keys[hash(item_key)] = repr(item)
        except TypeError:
            used_keys[repr(item_key)] = repr(item)
        yield item
pipeline_tag: text-classification
inference: yes
co2_eq_emissions: emissions: 0.2503482872622149
source: codecarbon
training_type: fine-tuning
on_cloud: no
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49160385131836
hours_used: 0.005
base_model: sentence-transformers/all-mpnet-base-v2
Likes: 0 | All-time downloads: 218
Created: 2024-01-25 09:31:48