mitra-mir/setfit-model-ESG-environmental

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:

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 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of water recycled is calculated as the volume, in thousands of cubic meters, recycled divided by the volume of water withdrawn.'

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}
}