metadata

base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Ce sont des travaux très pénibles qui nuisent à leur santé physique.
  - text: Besides, 4 disinfection spray machines provided to Patuakhali RC Unit.
  - text: >-
      Pese a los beneficios descritos anteriormente, Moody’s también advierte
      que la migración puede traer consigo un incremento en la tasa de desempleo
      de los trabajadores locales.
  - text: >-
      More people in NSAG/TBAF areas view things in a positive light now (41%)
      than in May (36%), but focal points in this AoC are still the least
      certain that precautionary measures will have an impact.
  - text: >-
      The observed spike was driven by the increased number of interviewed
      returnees’ households reporting poor food consumption: almost double from
      July to August 2020.
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.75
            name: Accuracy

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-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-multilingual-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 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
1	'HLPSS partners also held successful negotiations to halt a planned eviction of 559 IDPs from Biafra Camp Bulabulin in MMC LGA, when the IDPs were unable to meet landowners’ demands to pay between 500 to 1000 Naira monthly as rent since October 2019.' 'Sin embargo, un prestador del servicio de aseo encontró dificultad al momento de comprar: cepillos, guantes y escobas.' 'Conflict results in frequent civilian harm and atrocities have been committed in the area, including against children; populations are also subject to recurrent forced displacement.'
0	'En menor proporción y contrario a estos eventos, en Norte de Santander se reportaron afectaciones por la sequía propia de la temporada.' 'Cette situation est relativement meilleure comparé à la MAM mais l’objectif national de 70% n’est pas atteint.' 'These figures are consistent with those from the June and May consultations.'

Label

Examples

'HLPSS partners also held successful negotiations to halt a planned eviction of 559 IDPs from Biafra Camp Bulabulin in MMC LGA, when the IDPs were unable to meet landowners’ demands to pay between 500 to 1000 Naira monthly as rent since October 2019.'
'Sin embargo, un prestador del servicio de aseo encontró dificultad al momento de comprar: cepillos, guantes y escobas.'
'Conflict results in frequent civilian harm and atrocities have been committed in the area, including against children; populations are also subject to recurrent forced displacement.'

'En menor proporción y contrario a estos eventos, en Norte de Santander se reportaron afectaciones por la sequía propia de la temporada.'
'Cette situation est relativement meilleure comparé à la MAM mais l’objectif national de 70% n’est pas atteint.'
'These figures are consistent with those from the June and May consultations.'

Evaluation

Metrics

Label	Accuracy
all	0.75

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("osmedi/sentence_independancy_model")
# Run inference
preds = model("Ce sont des travaux très pénibles qui nuisent à leur santé physique.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	25.1481	78

Label	Training Sample Count
0	54
1	54

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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0037	1	0.3515	-
0.1852	50	0.2656	-
0.3704	100	0.1631	-
0.5556	150	0.0073	-
0.7407	200	0.0016	-
0.9259	250	0.001	-

Framework Versions

Python: 3.10.12
SetFit: 1.1.0
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Datasets: 3.0.1
Tokenizers: 0.19.1

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}
}