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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: If an acquirer of shares is not prepared to provide this declaration, the |
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Board may refuse to register him as a shareholder with the right to vote. |
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- text: The Company will also make every effort to improve the effectiveness of its |
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sustainability reporting. |
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- text: The Company maintains sufficient liquidity and has a variety of contingent |
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liquidity resources to manage liquidity across a range of economic scenarios. |
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- text: If we are unable to continue as a going concern, we may have to liquidate |
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our assets and may receive less than the value at which those assets are carried |
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on our audited financial statements, and it is likely that investors will lose |
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all or a part of their investment. |
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- text: In addition, factors such as failing to meet the expectations of or provide |
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quality medical care for our participants, adverse cyber or data security events, |
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adverse publicity or litigation involving or surrounding us, one of our centers |
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or our management, such as news articles and market rumors with respect to audits, |
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litigation and other processes described in these risk factors, have diminished |
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and may in the future diminish our reputation or that of our management and have |
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harmed and may in the future harm our brand, making it substantially more difficult |
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for us to attract new participants. |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8261964735516373 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 0.0 | <ul><li>'Our goal is for every employee to feel a strong sense of belonging and psychological safety.'</li><li>'Revenue from products labeled andor marketed to promote health and nutrition attributes is approximately 10.3 billion.'</li><li>'Assess climate change scenarios of key material risks.'</li></ul> | |
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| 1.0 | <ul><li>'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.'</li><li>'Board to review and make recommendations to shareholders concerning the composition of the Board of Directors, with particular focus on issues of independence.'</li><li>'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.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8262 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("mitra-mir/setfit-model-ESG-governance") |
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# Run inference |
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preds = model("The Company will also make every effort to improve the effectiveness of its sustainability reporting.") |
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``` |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 2 | 24.51 | 74 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0.0 | 146 | |
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| 1.0 | 54 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-----:|:----:|:-------------:|:---------------:| |
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| 0.002 | 1 | 0.479 | - | |
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| 0.1 | 50 | 0.2577 | - | |
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| 0.2 | 100 | 0.0589 | - | |
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| 0.3 | 150 | 0.0008 | - | |
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| 0.4 | 200 | 0.0004 | - | |
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| 0.5 | 250 | 0.0003 | - | |
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| 0.6 | 300 | 0.0002 | - | |
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| 0.7 | 350 | 0.0002 | - | |
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| 0.8 | 400 | 0.0002 | - | |
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| 0.9 | 450 | 0.0002 | - | |
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| 1.0 | 500 | 0.0002 | - | |
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### Framework Versions |
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- Python: 3.11.6 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.2.1 |
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- Transformers: 4.43.4 |
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- PyTorch: 2.4.1+cu121 |
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- Datasets: 3.0.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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