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: >-
      If an acquirer of shares is not prepared to provide this declaration, the
      Board may refuse to register him as a shareholder with the right to vote.
  - text: >-
      The Company will also make every effort to improve the effectiveness of
      its sustainability reporting.
  - text: >-
      The Company maintains sufficient liquidity and has a variety of contingent
      liquidity resources to manage liquidity across a range of economic
      scenarios.
  - text: >-
      If we are unable to continue as a going concern, we may have to liquidate
      our assets and may receive less than the value at which those assets are
      carried on our audited financial statements, and it is likely that
      investors will lose all or a part of their investment.
  - text: >-
      In addition, factors such as failing to meet the expectations of or
      provide quality medical care for our participants, adverse cyber or data
      security events, adverse publicity or litigation involving or surrounding
      us, one of our centers or our management, such as news articles and market
      rumors with respect to audits, litigation and other processes described in
      these risk factors, have diminished and may in the future diminish our
      reputation or that of our management and have harmed and may in the future
      harm our brand, making it substantially more difficult for us to attract
      new participants.
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: 0.8261964735516373
            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
0.0	'Our goal is for every employee to feel a strong sense of belonging and psychological safety.' 'Revenue from products labeled andor marketed to promote health and nutrition attributes is approximately 10.3 billion.' 'Assess climate change scenarios of key material risks.'
1.0	'We assessed accounting estimates for bias and evaluated whether the circumstances producing the bias, if any, represent a risk of material misstatement due to fraud.' 'Board to review and make recommendations to shareholders concerning the composition of the Board of Directors, with particular focus on issues of independence.' 'Our group audit mainly focused on significant group entities in terms of size and financial interest or where significant risks or complex activities were present, leading to full scope audits performed for including 2 nonconsolidated components.'

Label

Examples

0.0

'Our goal is for every employee to feel a strong sense of belonging and psychological safety.'
'Revenue from products labeled andor marketed to promote health and nutrition attributes is approximately 10.3 billion.'
'Assess climate change scenarios of key material risks.'

1.0

'We assessed accounting estimates for bias and evaluated whether the circumstances producing the bias, if any, represent a risk of material misstatement due to fraud.'
'Board to review and make recommendations to shareholders concerning the composition of the Board of Directors, with particular focus on issues of independence.'
'Our group audit mainly focused on significant group entities in terms of size and financial interest or where significant risks or complex activities were present, leading to full scope audits performed for including 2 nonconsolidated components.'

Evaluation

Metrics

Label	Accuracy
all	0.8262

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("mitra-mir/setfit-model-ESG-governance")
# Run inference
preds = model("The Company will also make every effort to improve the effectiveness of its sustainability reporting.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	24.51	74

Label	Training Sample Count
0.0	146
1.0	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
0.002	1	0.479	-
0.1	50	0.2577	-
0.2	100	0.0589	-
0.3	150	0.0008	-
0.4	200	0.0004	-
0.5	250	0.0003	-
0.6	300	0.0002	-
0.7	350	0.0002	-
0.8	400	0.0002	-
0.9	450	0.0002	-
1.0	500	0.0002	-

Framework Versions

Python: 3.11.6
SetFit: 1.1.0
Sentence Transformers: 3.2.1
Transformers: 4.43.4
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}
}