hojzas's picture
Add SetFit model
04c859e verified
metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
datasets:
  - hojzas/proj4-uniq_orig_order-lab1
metrics:
  - accuracy
widget:
  - text: return list(dict.fromkeys(it))
  - text: return [l for i, l in enumerate(it) if i == it.index(l)]
  - text: return list(dict.fromkeys(it).keys())
  - text: return [value for key, value in enumerate(it) if value not in it[:key]]
  - text: |2-
          registered = set()
          register = registered.add
          return [x for x in it if not (x in registered or register(x))]
pipeline_tag: text-classification
inference: true
co2_eq_emissions:
  emissions: 0.5122324218344383
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  ram_total_size: 251.49161911010742
  hours_used: 0.002
  hardware_used: 4 x NVIDIA RTX A5000
base_model: sentence-transformers/all-mpnet-base-v2

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

This is a SetFit model trained on the hojzas/proj4-uniq_orig_order-lab1 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 Sources

Model Labels

Label Examples
0
  • ' outputSequence = []\n for input in it:\n found = 0\n for output in outputSequence:\n if output == input:\n found = 1\n break\n if not found:\n outputSequence.append(input)\n return outputSequence'
  • ' uniq = []\n for char in it:\n if not char in uniq:\n uniq.append(char)\n return uniq'
  • 'return sorted(set(it), key=lambda y: it.index(y)) '
1
  • 'return [tmp for tmp in dict.fromkeys(it).keys()]'
  • 'return [i for i in dict.fromkeys(it)]'
  • 'return list(dict.fromkeys(it))'

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/proj4-uniq_orig_order-lab1")
# Run inference
preds = model("return list(dict.fromkeys(it))")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 20.9524 111
Label Training Sample Count
0 13
1 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
0.0189 1 0.1783 -
0.9434 50 0.0013 -

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.001 kg of CO2
  • Hours Used: 0.002 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}
}