metadata

base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: 'Colloqujdi Gio: Lodovico Vives latini, e volgari/Colloqui'
  - text: >-
      Ioannis Lodovici Vivis Von Underweÿsung ayner christlichen Frauwen drey
      Bücher ...erklärt unnd verteütscht. Durch Christophorum Brunonem .../Von
      Underweysung ayner christlichen Frauwen drey Bücher
  - text: >-
      Absolvtissimae in Hebraicam lingvam institvtiones accvratissime in vsvm
      studiosæ juuentutis conscriptæ ...Avtore Iohanne Isaaco Leuita
      Germano/Absolutissimae in Hebraicam linguam institutiones accuratissime in
      usum studiosæ juventutis conscriptæ ...  Autore Iohanne Isaaco Levita
      Germano
  - text: >-
      In tertiam partem D. Thomæ Aqvinatis commentaria Ioannis Wiggers ... a
      quæstione I. vsque ad quæstionem XXVI. de verbo incarnatoIn tertiam partem
      D. Thomae Aquinatis commentaria Ioannis Wiggers ... a quaestione I. usque
      ad quaestionem XXVI. de verbo incarnato
  - text: >-
      Tabvla in grammaticen Hebræam,authore Nicolao Clenardo. A Iohanne
      Quinquarboreo Aurilacensi à mendis quibus scatebat repurgata, &
      annotationibus illustrata./Tabula in grammaticen Hebraeam, authore Nicolao
      Clenardo. A Johanne Quinquarboreo Aurilacensi à mendis quibus scatebat
      repurgata, & annotationibus illustrata
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.735
            name: Accuracy

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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:

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

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

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
no	'Exomologesis sive Modus confitendi,per Erasmum Roterodamũ .../Exomologesis sive modus confitendi per Erasmum Roterodamum' 'Aen-wysinge van de macht en de eer die aen Jesus-Christus toe-komt. En van de eerbiedinghe die-men schuldigh is aen sijn aldersuyverste moeder Maria, en andere heyligen.' 'Staatkundige vermaningen en voorbeelden, die de deughden en zonden der vorsten betreffen.Nieuwelijks door I.H. Glazemaker vertaalt.'
yes	'Reclamations des trois états du duché de Brabant sur les atteintes portées a leurs droits et loix constitutionnelles au nom de S.M. Joseph II.' 'Brief van het Magistraet van Brugge van date 16 February 1788 aen de ordinaire Gedeputeerde der Staeten van Vlaenderen tenderende om staets gewyze te doen naedere Representatie tegen de opregtinge van een Seminarie Generael tot Loven ...' "Bericht voor d'Universiteyt &c. van Leuven, over de wijtloopige memorie, en andere schriften en documenten daer by, overgegeven aen haer Ho. Mog. door de vicarissen van Doornik"

Label

Examples

'Exomologesis sive Modus confitendi,per Erasmum Roterodamũ .../Exomologesis sive modus confitendi per Erasmum Roterodamum'
'Aen-wysinge van de macht en de eer die aen Jesus-Christus toe-komt. En van de eerbiedinghe die-men schuldigh is aen sijn aldersuyverste moeder Maria, en andere heyligen.'
'Staatkundige vermaningen en voorbeelden, die de deughden en zonden der vorsten betreffen.Nieuwelijks door I.H. Glazemaker vertaalt.'

yes

'Reclamations des trois états du duché de Brabant sur les atteintes portées a leurs droits et loix constitutionnelles au nom de S.M. Joseph II.'
'Brief van het Magistraet van Brugge van date 16 February 1788 aen de ordinaire Gedeputeerde der Staeten van Vlaenderen tenderende om staets gewyze te doen naedere Representatie tegen de opregtinge van een Seminarie Generael tot Loven ...'
"Bericht voor d'Universiteyt &c. van Leuven, over de wijtloopige memorie, en andere schriften en documenten daer by, overgegeven aen haer Ho. Mog. door de vicarissen van Doornik"

Evaluation

Metrics

Label	Accuracy
all	0.735

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("yannryanhelsinki/setfit-language-guess")
# Run inference
preds = model("Colloqujdi Gio: Lodovico Vives latini, e volgari/Colloqui")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	29.2759	92

Label	Training Sample Count
no	44
yes	72

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
0.0034	1	0.2242	-
0.1724	50	0.1951	-
0.3448	100	0.0342	-
0.5172	150	0.0008	-
0.6897	200	0.0006	-
0.8621	250	0.0003	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.39.0
PyTorch: 2.3.0+cu121
Datasets: 2.20.0
Tokenizers: 0.15.2

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}
}