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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      Quelles sont les règles en matière de garde d'enfants et de pension
      alimentaire ?
  - text: Comment se déroule une procédure de divorce ?
  - text: >-
      Quelles sont les principales difficultés rencontrées dans l'application de
      cette loi ?
  - text: Quels sont les régimes matrimoniaux possibles ?
  - text: >-
      Comment peut-on obtenir réparation pour un préjudice subi du fait d'une
      décision administrative illégale ?
pipeline_tag: text-classification
inference: true
base_model: intfloat/multilingual-e5-small
model-index:
  - name: SetFit with intfloat/multilingual-e5-small
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy
language:
  - fr
  - en

SetFit with intfloat/multilingual-e5-small

This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-small 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
independent
  • 'Comment rédiger un contrat de travail ?'
  • 'Quels sont les impôts et taxes applicables aux entreprises ?'
  • 'Comment peut-on contester un licenciement abusif ?'
follow_up
  • 'Quelles sont les conséquences de cette loi ?'
  • "Comment cette loi s'inscrit-elle dans le cadre plus large du droit algérien ?"
  • "Comment puis-je obtenir plus d'informations sur ce sujet ?"

Evaluation

Metrics

Label Accuracy
all 1.0

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("super-cinnamon/fewshot-followup-multi-e5")
# Run inference
preds = model("Comment se déroule une procédure de divorce ?")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 9.6184 16
Label Training Sample Count
independent 43
follow_up 33

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0027 1 0.3915 -
0.1326 50 0.3193 -
0.2653 100 0.2252 -
0.3979 150 0.1141 -
0.5305 200 0.0197 -
0.6631 250 0.0019 -
0.7958 300 0.0021 -
0.9284 350 0.0002 -
1.0610 400 0.0008 -
1.1936 450 0.0005 -
1.3263 500 0.0002 -
1.4589 550 0.0002 -
1.5915 600 0.0007 -
1.7241 650 0.0001 -
1.8568 700 0.0003 -
1.9894 750 0.0002 -
2.1220 800 0.0001 -
2.2546 850 0.0002 -
2.3873 900 0.0 -
2.5199 950 0.0003 -
2.6525 1000 0.0001 -
2.7851 1050 0.0001 -
2.9178 1100 0.0001 -
3.0504 1150 0.0001 -
3.1830 1200 0.0001 -
3.3156 1250 0.0001 -
3.4483 1300 0.0001 -
3.5809 1350 0.0001 -
3.7135 1400 0.0 -
3.8462 1450 0.0 -
3.9788 1500 0.0 -
4.1114 1550 0.0 -
4.2440 1600 0.0001 -
4.3767 1650 0.0001 -
4.5093 1700 0.0001 -
4.6419 1750 0.0001 -
4.7745 1800 0.0 -
4.9072 1850 0.0001 -
5.0398 1900 0.0 -
5.1724 1950 0.0001 -
5.3050 2000 0.0 -
5.4377 2050 0.0001 -
5.5703 2100 0.0 -
5.7029 2150 0.0 -
5.8355 2200 0.0 -
5.9682 2250 0.0001 -
6.1008 2300 0.0001 -
6.2334 2350 0.0 -
6.3660 2400 0.0001 -
6.4987 2450 0.0 -
6.6313 2500 0.0 -
6.7639 2550 0.0 -
6.8966 2600 0.0 -
7.0292 2650 0.0 -
7.1618 2700 0.0 -
7.2944 2750 0.0 -
7.4271 2800 0.0001 -
7.5597 2850 0.0 -
7.6923 2900 0.0 -
7.8249 2950 0.0 -
7.9576 3000 0.0 -
8.0902 3050 0.0 -
8.2228 3100 0.0 -
8.3554 3150 0.0 -
8.4881 3200 0.0001 -
8.6207 3250 0.0 -
8.7533 3300 0.0 -
8.8859 3350 0.0 -
9.0186 3400 0.0001 -
9.1512 3450 0.0 -
9.2838 3500 0.0 -
9.4164 3550 0.0001 -
9.5491 3600 0.0 -
9.6817 3650 0.0001 -
9.8143 3700 0.0 -
9.9469 3750 0.0001 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu118
  • Datasets: 2.15.0
  • Tokenizers: 0.15.0

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}
}