oneryalcin/finepdfs-purpose-setfit-neomme-v2

SetFit with Hcompany/NeoMME-260M-Retriever-ST-dense

This is a SetFit model that can be used for Text Classification. This SetFit model uses Hcompany/NeoMME-260M-Retriever-ST-dense 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. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 2.Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• —Model Type: SetFit
• —Sentence Transformer body: Hcompany/NeoMME-260M-Retriever-ST-dense
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 16384 tokens
• —Number of Classes: 6 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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

Model Labels

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from PIL import Image

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("oneryalcin/finepdfs-purpose-setfit-neomme-v2")
# Run inference on images
preds = model([Image.open("example.png")])

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Training Details

Training Hyperparameters

• —batch_size: (32, 32)
• —num_epochs: (1, 1)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 3
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

Framework Versions

• —Python: 3.12.10
• —SetFit: 1.3.0.dev0
• —Sentence Transformers: 6.0.1
• —Transformers: 5.17.0
• —PyTorch: 2.14.0+cu130
• —Datasets: 5.0.1
• —Tokenizers: 0.23.2

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}
}

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Evaluation (oneryalcin/finepdfs-purpose-pages-v2, split test)

accuracy 0.681, macro F1 0.638 on 702 images; 800 training images per class; body Hcompany/NeoMME-260M-Retriever-ST-dense, task document; trained in 1056s on cuda.

                       precision    recall  f1-score   support

administration_policy       0.59      0.70      0.64        67
  exercise_assessment       0.71      0.81      0.76       118
instruction_reference       0.66      0.61      0.63       220
       news_promotion       0.63      0.70      0.66       132
                other       0.47      0.29      0.36        24
    research_analysis       0.83      0.72      0.77       141

             accuracy                           0.68       702
            macro avg       0.65      0.64      0.64       702
         weighted avg       0.68      0.68      0.68       702