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: and importance of the climate crisis requires everyone to play their part.
  - text: >-
      The Group has unused tax losses carried forward of 512m, primarily UK
      capital losses, on which no deferred tax is recognised.
  - 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.
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: 0.7657430730478589
            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	'We believe that no company should prosper while violating the basic human rights of others whether through unlawful slavery, servitude, forced or compulsory labor, or otherwise exploitative means.' 'The decreases in the current period were offset, in part, by increases in conference and training expenditures incurred.' 'Environmental Responsibility As a core part of our business, we continually monitor, assess and respond not only to the risks but also to the opportunities posed by changing climate conditions.'
1.0	'In addition, we have a majority standard for the election of directors on our board.' 'We generally find that it is more effective to take a collaborative approach in identifying and addressing proposed regulatory changes related to our assets and operations.' 'Regulations of the Supervisory Board The tasks, responsibilities and internal procedural matters for the Supervisory Board are addressed in the Regulations of the Supervisory Board and are available on our corporate website.'

Label

Examples

0.0

'We believe that no company should prosper while violating the basic human rights of others whether through unlawful slavery, servitude, forced or compulsory labor, or otherwise exploitative means.'
'The decreases in the current period were offset, in part, by increases in conference and training expenditures incurred.'
'Environmental Responsibility As a core part of our business, we continually monitor, assess and respond not only to the risks but also to the opportunities posed by changing climate conditions.'

1.0

'In addition, we have a majority standard for the election of directors on our board.'
'We generally find that it is more effective to take a collaborative approach in identifying and addressing proposed regulatory changes related to our assets and operations.'
'Regulations of the Supervisory Board The tasks, responsibilities and internal procedural matters for the Supervisory Board are addressed in the Regulations of the Supervisory Board and are available on our corporate website.'

Evaluation

Metrics

Label	Accuracy
all	0.7657

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-environmental")
# Run inference
preds = model("and importance of the climate crisis requires everyone to play their part.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	25.4020	72

Label	Training Sample Count
0.0	148
1.0	51

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
0.0020	1	0.4091	-
0.1004	50	0.1992	-
0.2008	100	0.0104	-
0.3012	150	0.0006	-
0.4016	200	0.0003	-
0.5020	250	0.0002	-
0.6024	300	0.0002	-
0.7028	350	0.0001	-
0.8032	400	0.0001	-
0.9036	450	0.0001	-
0.0020	1	0.25	-
0.1004	50	0.349	-
0.2008	100	0.047	-
0.3012	150	0.0172	-
0.4016	200	0.0023	-
0.5020	250	0.0002	-
0.6024	300	0.0002	-
0.7028	350	0.0003	-
0.8032	400	0.0001	-
0.9036	450	0.0001	-
0.0020	1	0.3684	-
0.1004	50	0.39	-
0.2008	100	0.1277	-
0.3012	150	0.0064	-
0.4016	200	0.0006	-
0.5020	250	0.0004	-
0.6024	300	0.0003	-
0.7028	350	0.0003	-
0.8032	400	0.0002	-
0.9036	450	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}
}