Model: hojzas/setfit-proj8
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/paraphrase-mpnet-base-v2, base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2, co2_eq_emissions, region:us
Base models (finetune): id: sentence-transformers/paraphrase-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 x: x):\n    keys_used = {}\n    for item in iterable:\n        rp = repr(key(item))\n        if rp not in keys_used.keys():\n            keys_used[rp] = repr(item)\n            yield item, def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i)), def first_with_given_key(iterable, key=repr):\n    set_of_keys = set()\n    lambda_key = (lambda x: key(x))\n    for item in iterable:\n        key = lambda_key(item)\n        try:\n            key_for_set = hash(key)\n        except TypeError:\n            key_for_set = repr(key)\n        if key_for_set in set_of_keys:\n            continue\n        set_of_keys.add(key_for_set)\n        yield item, def first_with_given_key(iterable, key = lambda x: x):\n    found_keys={}\n    for i in iterable:\n        if key(i) not in found_keys.keys():\n            found_keys[key(i)]=i\n            yield i, def first_with_given_key(the_iterable, key=lambda x: x):\n    temp_keys=[]\n    for i in range(len(the_iterable)):\n        if (key(the_iterable[i]) not in temp_keys):\n            temp_keys.append(key(the_iterable[i]))\n            yield the_iterable[i]\n    del temp_keys
pipeline_tag: text-classification
inference: yes
co2_eq_emissions: emissions: 0.2520929621561019
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/paraphrase-mpnet-base-v2
Likes: 0 | All-time downloads: 165
Created: 2024-01-24 17:40:07