hojzas/proj8-lab2

SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model trained on the hojzas/proj8-lab2 dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 384 tokens
• Number of Classes: 3 classes
• Training Dataset: hojzas/proj8-lab2

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
0	'def first_with_given_key(iterable, key=lambda x: x):\n keys_in_list = []\n for it in iterable:\n if key(it) not in keys_in_list:\n keys_in_list.append(key(it))\n yield it' 'def first_with_given_key(iterable, key=lambda value: value):\n it = iter(iterable)\n saved_keys = []\n while True:\n try:\n value = next(it)\n if key(value) not in saved_keys:\n saved_keys.append(key(value))\n yield value\n except StopIteration:\n break' 'def first_with_given_key(iterable, key=None):\n if key is None:\n key = lambda x: x\n item_list = []\n key_set = set()\n for item in iterable:\n generated_item = key(item)\n if generated_item not in item_list:\n item_list.append(generated_item)\n yield item'
2	'def first_with_given_key(iterable, key=repr):\n prev_keys = {}\n lamb_key = lambda item: key(item)\n for obj in iterable:\n obj_key = lamb_key(obj)\n if(obj_key) in prev_keys.keys():\n continue\n try:\n prev_keys[hash(obj_key)] = repr(obj)\n except TypeError:\n prev_keys[repr(obj_key)] = repr(obj)\n yield obj' 'def first_with_given_key(iterable, key=repr):\n used_keys = dict()\n get_key = lambda index: key(index)\n for index in iterable:\n index_key = get_key(index)\n if index_key in used_keys.keys():\n continue\n try:\n used_keys[hash(index_key)] = repr(index)\n except TypeError:\n used_keys[repr(index_key)] = repr(index)\n yield index' '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'
1	'def first_with_given_key(lst, key = lambda x: x):\n res = set()\n for i in lst:\n if repr(key(i)) not in res:\n res.add(repr(key(i)))\n yield 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=None):\n if key is None:\n key = identity\n appeared_keys = set()\n for item in iterable:\n generated_key = key(item)\n if not generated_key.hash:\n generated_key = repr(generated_key)\n if generated_key not in appeared_keys:\n appeared_keys.add(generated_key)\n yield item'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/proj8-lab2")
# Run inference
preds = model("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))")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	43	92.2069	125

Label	Training Sample Count
0	13
1	8
2	8

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (2e-05, 2e-05)
• head_learning_rate: 2e-05
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0137	1	0.4142	-
0.6849	50	0.0024	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Carbon Emitted: 0.002 kg of CO2
• Hours Used: 0.006 hours

Training Hardware

• On Cloud: No
• GPU Model: 4 x NVIDIA RTX A5000
• CPU Model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
• RAM Size: 251.49 GB

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.2.2
• Transformers: 4.36.1
• PyTorch: 2.1.2+cu121
• Datasets: 2.14.7
• Tokenizers: 0.15.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}