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amplyfi
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gte-base-en-v1.5_annotations_cache_aggregated_multilabel

Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
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metadata
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: The Philosophical Enigma of Large Language Models
  - text: CONSTITUTIONAL AND LEGAL REGULATION OF THE STATE CIVIL SERVICE
  - text: Qashio and YallaCompare launch 'Qashio Insurance'
  - text: >-
      Online Travel Accommodations Market Report 2024 Reveals The Global Number
      Of Travel App Downloads Surpassed 3 Billion In 2023
  - text: >-
      The Procter & Gamble Company (NYSE:PG) Stock Position Decreased by
      CarsonAllaria Wealth Management Ltd.
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: OysterHR/gte-base-en-v1.5

SetFit with OysterHR/gte-base-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses OysterHR/gte-base-en-v1.5 as the Sentence Transformer embedding model. A OneVsRestClassifier 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: OysterHR/gte-base-en-v1.5
  • Classification head: a OneVsRestClassifier instance
  • Maximum Sequence Length: 8192 tokens

Model Sources

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("amplyfi/gte-base-en-v1.5_annotations_cache_aggregated_multilabel")
# Run inference
preds = model("The Philosophical Enigma of Large Language Models")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 11.0917 30

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0017 1 0.4182 -
0.0833 50 0.2867 -
0.1667 100 0.25 -
0.25 150 0.2203 -
0.3333 200 0.1984 -
0.4167 250 0.1759 -
0.5 300 0.1555 -
0.5833 350 0.1336 -
0.6667 400 0.1306 -
0.75 450 0.1245 -
0.8333 500 0.121 -
0.9167 550 0.1166 -
1.0 600 0.1139 -
1.0833 650 0.1083 -
1.1667 700 0.102 -
1.25 750 0.0965 -
1.3333 800 0.1027 -
1.4167 850 0.1045 -
1.5 900 0.1069 -
1.5833 950 0.0935 -
1.6667 1000 0.0929 -
1.75 1050 0.0875 -
1.8333 1100 0.0906 -
1.9167 1150 0.0999 -
2.0 1200 0.0974 -
2.0833 1250 0.0877 -
2.1667 1300 0.0776 -
2.25 1350 0.0839 -
2.3333 1400 0.0895 -
2.4167 1450 0.0819 -
2.5 1500 0.0819 -
2.5833 1550 0.0913 -
2.6667 1600 0.0881 -
2.75 1650 0.0921 -
2.8333 1700 0.0839 -
2.9167 1750 0.0851 -
3.0 1800 0.088 -
3.0833 1850 0.0801 -
3.1667 1900 0.086 -
3.25 1950 0.0831 -
3.3333 2000 0.0747 -
3.4167 2050 0.0773 -
3.5 2100 0.0832 -
3.5833 2150 0.078 -
3.6667 2200 0.0856 -
3.75 2250 0.0797 -
3.8333 2300 0.0759 -
3.9167 2350 0.0846 -
4.0 2400 0.0833 -
4.0833 2450 0.0767 -
4.1667 2500 0.0787 -
4.25 2550 0.0743 -
4.3333 2600 0.077 -
4.4167 2650 0.0808 -
4.5 2700 0.0768 -
4.5833 2750 0.0808 -
4.6667 2800 0.0796 -
4.75 2850 0.077 -
4.8333 2900 0.0787 -
4.9167 2950 0.071 -
5.0 3000 0.0773 -
5.0833 3050 0.069 -
5.1667 3100 0.0795 -
5.25 3150 0.0748 -
5.3333 3200 0.075 -
5.4167 3250 0.0745 -
5.5 3300 0.076 -
5.5833 3350 0.0708 -
5.6667 3400 0.0788 -
5.75 3450 0.0803 -
5.8333 3500 0.0756 -
5.9167 3550 0.0737 -
6.0 3600 0.073 -
6.0833 3650 0.066 -
6.1667 3700 0.0735 -
6.25 3750 0.0733 -
6.3333 3800 0.0754 -
6.4167 3850 0.0717 -
6.5 3900 0.0772 -
6.5833 3950 0.0695 -
6.6667 4000 0.0734 -
6.75 4050 0.0709 -
6.8333 4100 0.0776 -
6.9167 4150 0.073 -
7.0 4200 0.0732 -
7.0833 4250 0.069 -
7.1667 4300 0.0685 -
7.25 4350 0.0681 -
7.3333 4400 0.075 -
7.4167 4450 0.0751 -
7.5 4500 0.075 -
7.5833 4550 0.0686 -
7.6667 4600 0.07 -
7.75 4650 0.0716 -
7.8333 4700 0.0749 -
7.9167 4750 0.0687 -
8.0 4800 0.0753 -
8.0833 4850 0.0661 -
8.1667 4900 0.0662 -
8.25 4950 0.0725 -
8.3333 5000 0.0701 -
8.4167 5050 0.0702 -
8.5 5100 0.0755 -
8.5833 5150 0.0698 -
8.6667 5200 0.0686 -
8.75 5250 0.0659 -
8.8333 5300 0.0758 -
8.9167 5350 0.0702 -
9.0 5400 0.0721 -
9.0833 5450 0.071 -
9.1667 5500 0.0652 -
9.25 5550 0.0657 -
9.3333 5600 0.0742 -
9.4167 5650 0.0725 -
9.5 5700 0.066 -
9.5833 5750 0.068 -
9.6667 5800 0.0709 -
9.75 5850 0.0645 -
9.8333 5900 0.0669 -
9.9167 5950 0.0696 -
10.0 6000 0.0692 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.1
  • Sentence Transformers: 3.3.1
  • Transformers: 4.48.0.dev0
  • PyTorch: 2.5.1+cu124
  • Datasets: 3.1.0
  • Tokenizers: 0.21.0

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}
}