Model: hojzas/proj8-lab1
Author: hojzas
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, dataset:hojzas/proj8-lab1, arxiv:2209.11055, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, model-index, 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-lab1
metrics: accuracy
widget: def first_with_given_key(iterable, key=repr):
    res = []
    keys = set()
    for item in iterable:
        if key(item) not in keys:
            keys.add(key(item))
    return res, def first_with_given_key(iterable, key=repr):
	get_key = get_key_l(key)
	used_keys = []
	for item in iterable:
		key_item = get_key(item)
			
		if key_item in used_keys:
			continue
		
		try:
			used_keys.append(hash(key_item))
		except TypeError:
			used_keys.apppend(repr(key_item))
			
		yield item, def first_with_given_key(iterable, key=repr):
    set_of_keys = set()
    key_lambda = _get_lambda(key)
    for item in iterable:
        key = key_lambda(item)
        try:
            key_to_set = hash(key)
        except TypeError:
            key_to_set = repr(key)

        if key_to_set in set_of_keys:
            continue
        set_of_keys.add(key_to_set)
        yield item, def first_with_given_key(iterable, key=lambda y: y):
    result = list()
    func_it = iter(iterable)
    while True:
        try:
            value = next(func_it)
            if key(value) not in result:
                yield value
                result.insert(-1, key(value))
        except StopIteration:
            break, 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: 2.0314927247192536
source: codecarbon
training_type: fine-tuning
on_cloud: no
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49161911010742
hours_used: 0.006
hardware_used: 4 x NVIDIA RTX A5000
base_model: sentence-transformers/all-mpnet-base-v2
model-index: name: SetFit with sentence-transformers/all-mpnet-base-v2
results: task: type: text-classification
name: Text Classification
dataset: name: hojzas/proj8-lab1
type: hojzas/proj8-lab1
split: test
metrics: type: accuracy
value: 0.9722222222222222
name: Accuracy
verified: no
Likes: 0 | All-time downloads: 12
Created: 2024-01-31 15:40:18