Hugging Face's logo

faodl
/
model_g20_multilabel

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files Community
model_g20_multilabel / README.md
lgsilvaesilva's picture
lgsilvaesilva
Push model using huggingface_hub.
44eec2e verified
preview code
|
raw
history blame contribute delete
10.8 kB
metadata
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      6) Implement advocacy strategies with heads of government ministries,
      departments 


      and institutions, national, district and local leaders on solutions to
      major nutrition 

      problems.
  - text: >+
      The Government plans to continue its interventions aimed at increasing
      access to drinking water by:

      - in rural areas, constructing an additional 2 500 water points (mainly
      boreholes) and rehabilitating an extra 2 000 existing water points.

      - in urban and pre-urban areas, rehabilitating and constructing water
      supply infrastructure in the various urban towns.

      The Government will also, in terms of sanitation, continue to promote
      community-based approaches and construct facilities.


      Objective: ensure adequate access to sanitation facilities and increase
      access to clean and safe drinking water from 64% (2014) to 67% of the
      population in urban areas and from 83% (2014) to 85% in urban and
      pre-urban areas.

  - text: >-
      Specific objective

      i) to improve safe water supply services to the people in the rural
      communities

      ii) to improve the water supply service levels in rural area to enable
      rural the population in the 

      project areas to increase their economic income through incorporating back
      yard or mini 

      irrigation system.
  - text: |-
      Social security contributions  

      Labor 
      Markets 

      Activation measures  

      • During the period of state of emergency, all training activities 
      recognized by the Ministry of Labor and Social Protection can be 
      delivered online.
  - text: "Training infrastructure will be adapted to accommodate \nnew\tprogrammes."
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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 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("faodl/model_g20_multilabel")
# Run inference
preds = model("Training infrastructure will be adapted to accommodate 
new	programmes.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 48.9866 1181

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 50
  • 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
0.0002 1 0.2348 -
0.0119 50 0.1747 -
0.0237 100 0.153 -
0.0356 150 0.1314 -
0.0475 200 0.1263 -
0.0593 250 0.1168 -
0.0712 300 0.116 -
0.0831 350 0.098 -
0.0949 400 0.1085 -
0.1068 450 0.0975 -
0.1187 500 0.094 -
0.1305 550 0.082 -
0.1424 600 0.0856 -
0.1543 650 0.0838 -
0.1662 700 0.0762 -
0.1780 750 0.0722 -
0.1899 800 0.0722 -
0.2018 850 0.0634 -
0.2136 900 0.0584 -
0.2255 950 0.0664 -
0.2374 1000 0.0688 -
0.2492 1050 0.0629 -
0.2611 1100 0.0579 -
0.2730 1150 0.0652 -
0.2848 1200 0.0573 -
0.2967 1250 0.0584 -
0.3086 1300 0.0558 -
0.3204 1350 0.0586 -
0.3323 1400 0.0574 -
0.3442 1450 0.0444 -
0.3560 1500 0.0462 -
0.3679 1550 0.0488 -
0.3798 1600 0.0505 -
0.3916 1650 0.0529 -
0.4035 1700 0.0487 -
0.4154 1750 0.0459 -
0.4272 1800 0.0531 -
0.4391 1850 0.0448 -
0.4510 1900 0.0382 -
0.4629 1950 0.0457 -
0.4747 2000 0.0493 -
0.4866 2050 0.0488 -
0.4985 2100 0.049 -
0.5103 2150 0.0495 -
0.5222 2200 0.0402 -
0.5341 2250 0.0493 -
0.5459 2300 0.0496 -
0.5578 2350 0.0438 -
0.5697 2400 0.0361 -
0.5815 2450 0.0428 -
0.5934 2500 0.0419 -
0.6053 2550 0.0416 -
0.6171 2600 0.0338 -
0.6290 2650 0.0397 -
0.6409 2700 0.0385 -
0.6527 2750 0.0285 -
0.6646 2800 0.0461 -
0.6765 2850 0.0341 -
0.6883 2900 0.0379 -
0.7002 2950 0.0435 -
0.7121 3000 0.0341 -
0.7239 3050 0.0395 -
0.7358 3100 0.0424 -
0.7477 3150 0.0415 -
0.7596 3200 0.0422 -
0.7714 3250 0.0402 -
0.7833 3300 0.0309 -
0.7952 3350 0.0379 -
0.8070 3400 0.039 -
0.8189 3450 0.0427 -
0.8308 3500 0.0331 -
0.8426 3550 0.0457 -
0.8545 3600 0.0306 -
0.8664 3650 0.034 -
0.8782 3700 0.0354 -
0.8901 3750 0.0393 -
0.9020 3800 0.036 -
0.9138 3850 0.0339 -
0.9257 3900 0.0332 -
0.9376 3950 0.0274 -
0.9494 4000 0.0372 -
0.9613 4050 0.0319 -
0.9732 4100 0.0339 -
0.9850 4150 0.0349 -
0.9969 4200 0.0383 -

Framework Versions

  • Python: 3.11.13
  • SetFit: 1.1.2
  • Sentence Transformers: 4.1.0
  • Transformers: 4.52.4
  • PyTorch: 2.6.0+cu124
  • Datasets: 3.6.0
  • Tokenizers: 0.21.1

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}
}