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: 0.9571788413098237
            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	'The concentration of our sales in our fourth fiscal quarter increases this impact as the revenue impact of most fourth fiscal quarter subscription sales will not be realized until the following fiscal year.' 'The deposit insurance fund of the FDIC insures deposit accounts in HomeTrust Bank up to 250,000 per separately insured deposit ownership right or category.' 'On 19 October 2020 the Company entered into a 25 million revolving credit facility agreement with State Street Bank International GmbH.'
1.0	'The incredible rise in the price of fuel and emission allowances shaped the trajectory of our most important wholesale electricity markets in Europe.' 'As part of this process we understood our impact linked to home working as a new material source of carbon emissions.' 'Per SASB Industry Standard (October 2018) for Iron Steel Producers, the percentage of water recycled is calculated as the volume, in thousands of cubic meters, recycled divided by the volume of water withdrawn.'

Label

Examples

0.0

'The concentration of our sales in our fourth fiscal quarter increases this impact as the revenue impact of most fourth fiscal quarter subscription sales will not be realized until the following fiscal year.'
'The deposit insurance fund of the FDIC insures deposit accounts in HomeTrust Bank up to 250,000 per separately insured deposit ownership right or category.'
'On 19 October 2020 the Company entered into a 25 million revolving credit facility agreement with State Street Bank International GmbH.'

1.0

'The incredible rise in the price of fuel and emission allowances shaped the trajectory of our most important wholesale electricity markets in Europe.'
'As part of this process we understood our impact linked to home working as a new material source of carbon emissions.'
'Per SASB Industry Standard (October 2018) for Iron Steel Producers, the percentage of water recycled is calculated as the volume, in thousands of cubic meters, recycled divided by the volume of water withdrawn.'

Evaluation

Metrics

Label	Accuracy
all	0.9572

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	4	24.5578	112

Label	Training Sample Count
0.0	149
1.0	50

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	0.3928	-
0.1004	50	0.1952	-
0.2008	100	0.0054	-
0.3012	150	0.0004	-
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	-

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}
}